Ensemble decadal predictions from analysed initial conditions

2007 ◽  
Vol 365 (1857) ◽  
pp. 2179-2191 ◽  
Author(s):  
Alberto Troccoli ◽  
T.N Palmer
Keyword(s):  
Time Scales ◽  
Initial Conditions ◽  
Coupled Model ◽  
Seasonal Prediction ◽  
Model Configuration ◽  
Global Cooling ◽  
The Impact ◽  
Climatic Signals ◽  
Different Levels ◽  
Initialization Procedure

Sensitivity experiments using a coupled model initialized from analysed atmospheric and oceanic observations are used to investigate the potential for interannual-to-decadal predictability. The potential for extending seasonal predictions to longer time scales is explored using the same coupled model configuration and initialization procedure as used for seasonal prediction. It is found that, despite model drift, climatic signals on interannual-to-decadal time scales appear to be detectable. Two climatic states have been chosen: one starting in 1965, i.e. ahead of a period of global cooling, and the other in 1994, ahead of a period of global warming. The impact of initial conditions and of the different levels of greenhouse gases are isolated in order to gain insights into the source of predictability.

scholarly journals Enhanced Seasonal Prediction of European Winter Warming following Volcanic Eruptions

2009 ◽  
Vol 22 (23) ◽  
pp. 6168-6180 ◽  
Author(s):  
A. G. Marshall ◽  
A. A. Scaife ◽  
S. Ineson
Keyword(s):  
Climate Model ◽  
Initial Conditions ◽  
Polar Vortex ◽  
Volcanic Eruptions ◽  
Seasonal Prediction ◽  
The Arctic ◽  
Extended Model ◽  
Similar Response ◽  
Volcanic Aerosol ◽  
The Impact

Abstract The impact of explosive volcanic eruptions on the atmospheric circulation at high northern latitudes is assessed in two versions of the Met Office Hadley Centre’s atmospheric climate model. The standard version of the model extends to an altitude of around 40 km, while the extended version has enhanced stratospheric resolution and reaches 85-km altitude. Seasonal hindcasts initialized on 1 December produce a strengthening of the winter polar vortex and anomalous warming over northern Europe characteristic of the positive phase of the Arctic Oscillation (AO) when forced with volcanic aerosol following the 1963 Mount Agung, 1982 El Chichón, and 1991 Mount Pinatubo eruptions, as is observed. The AO signal in the extended model is of comparable strength to that in the standard model, showing that there is little impact from both increasing the vertical resolution in the stratosphere and extending the model domain to near the mesopause. The presence of this signal in the models, however, is likely due to the persistence of the observed signal from the initial conditions, because a similar set of experiments initiated with the same conditions, but with no volcanic aerosol forcing, exhibits a similar response as the forced runs. This suggests that the model has limited fidelity in capturing the response to volcanic aerosols on its own, consistent with previous studies on the impact of volcanic forcing in long climate simulations, but does support the premise that seasonal winter forecasts are substantially improved with the inclusion of stratospheric information.

scholarly journals The INGV–CMCC Seasonal Prediction System: Improved Ocean Initial Conditions

2010 ◽  
Vol 138 (7) ◽  
pp. 2930-2952 ◽  
Author(s):  
Andrea Alessandri ◽  
Andrea Borrelli ◽  
Simona Masina ◽  
Annalisa Cherchi ◽  
Silvio Gualdi ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Seasonal Prediction ◽  
Global Scale ◽  
Tropical Pacific ◽  
Optimal Interpolation ◽  
Boreal Winter ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Climate Anomalies ◽  
The Impact

Abstract The development of the Istituto Nazionale di Geofisica e Vulcanologia (INGV)–Centro Euro-Mediterraneo per i Cambiamenti Climatici (CMCC) Seasonal Prediction System (SPS) is documented. In this SPS the ocean initial-conditions estimation includes a reduced-order optimal interpolation procedure for the assimilation of temperature and salinity profiles at the global scale. Nine-member ensemble forecasts have been produced for the period 1991–2003 for two starting dates per year in order to assess the impact of the subsurface assimilation in the ocean for initialization. Comparing the results with control simulations (i.e., without assimilation of subsurface profiles during ocean initialization), it is shown that the improved ocean initialization increases the skill in the prediction of tropical Pacific sea surface temperatures of the system for boreal winter forecasts. Considering the forecast of the 1997/98 El Niño, the data assimilation in the ocean initial conditions leads to a considerable improvement in the representation of its onset and development. The results presented in this paper indicate a better prediction of global-scale surface climate anomalies for the forecasts started in November, probably because of the improvement in the tropical Pacific. For boreal winter, significant increases in the capability of the system to discriminate above-normal and below-normal temperature anomalies are shown in both the tropics and extratropics.

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 How sensitive are modeled contemporary subsea permafrost thaw and thickness of the methane clathrates stability zone in Eurasian Arctic to assumptions on Pleistocene glacial cycles?

2016 ◽  
Author(s):  
Valentina V. Malakhova ◽  
Alexey V. Eliseev
Keyword(s):  
Time Scales ◽  
Thermal State ◽  
Initial Conditions ◽  
Single Point ◽  
Ice Core ◽  
Oceanic Water ◽  
Stability Zone ◽  
Glacial Cycles ◽  
The Impact ◽  
Subsea Permafrost

Abstract. Single-point simulations with a model for thermal state of subsea sediments driven by the forcing constructed from the ice core data show that the impact of initial conditions is lost after ~ 100 kyr. The time scales of temperature propagation in sediments and respective permafrost response are ~ 10–20 kyr which is longer than the present interglacial. The timings of shelf exposure during oceanic regressions and flooding during transgressions are important for representation of sediment thermal state and hydrates stability zone (HSZ). These timings should depend on the contemporary shelf depth (SD). During glacial cycles temperature at the top of sediments is a major driver of HSZ vertical boundaries change for SD of few tens of meters, while the pressure exerted by oceanic water becomes more important for larger SD. Thus, even the existence of HSZ and its disappearance might not be easily tied to oceanic transgressions and regressions.

A Study of Impacts of Coupled Model Initial Shocks and State–Parameter Optimization on Climate Predictions Using a Simple Pycnocline Prediction Model

2011 ◽  
Vol 24 (23) ◽  
pp. 6210-6226 ◽  
Author(s):  
S. Zhang
Keyword(s):  
Time Scales ◽  
Parameter Optimization ◽  
Time Scale ◽  
Model Simulation ◽  
Coupled Model ◽  
State Parameter ◽  
Model Parameters ◽  
Forecast Errors ◽  
Long Time ◽  
The Impact

Abstract A skillful decadal prediction that foretells varying regional climate conditions over seasonal–interannual to multidecadal time scales is of societal significance. However, predictions initialized from the climate-observing system tend to drift away from observed states toward the imperfect model climate because of the model biases arising from imperfect model equations, numeric schemes, and physical parameterizations, as well as the errors in the values of model parameters. Here, a simple coupled model that simulates the fundamental features of the real climate system and a “twin” experiment framework are designed to study the impact of initialization and parameter optimization on decadal predictions. One model simulation is treated as “truth” and sampled to produce “observations” that are assimilated into other simulations to produce observation-estimated states and parameters. The degree to which the model forecasts based on different estimates recover the truth is an assessment of the impact of coupled initial shocks and parameter optimization on climate predictions of interests. The results show that the coupled model initialization through coupled data assimilation in which all coupled model components are coherently adjusted by observations minimizes the initial coupling shocks that reduce the forecast errors on seasonal–interannual time scales. Model parameter optimization with observations effectively mitigates the model bias, thus constraining the model drift in long time-scale predictions. The coupled model state–parameter optimization greatly enhances the model predictability. While valid “atmospheric” forecasts are extended 5 times, the decadal predictability of the “deep ocean” is almost doubled. The coherence of optimized model parameters and states is critical to improve the long time-scale predictions.

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>

Evaluation of FIO-ESM v1.0 Seasonal Prediction Skills Over the North Pacific

2021 ◽  
Author(s):  
Yajuan Song ◽  
Xunqiang Yin
Keyword(s):  
Data Assimilation ◽  
North Pacific ◽  
Climate Model ◽  
Initial Conditions ◽  
Coupled Model ◽  
Seasonal Prediction ◽  
Optimal Interpolation ◽  
The North ◽  
Prediction Systems ◽  
The North Pacific

<p>Accurate prediction over the North Pacific, especially for the key parameter of sea<br>surface temperature (SST), remains a challenge for short-term climate prediction. In<br>this study, seasonal predicted skills of the First Institute of Oceanography Earth System<br>Model version 1.0 (FIO-ESM v1.0) over the North Pacific were assessed. Ensemble<br>adjustment Kalman filter (EAKF) and Projection Optimal Interpolation (Projection-OI) data<br>assimilation schemes were used to provide initial conditions for FIO-ESM v1.0 hindcasts<br>that were started from the first day of each month between 1993 and 2017. Evolution<br>and spacial distribution of SST anomalies over the North Pacific were reasonably<br>reproduced in EAKF and Projection-OI assimilation output. Two hindcast experiments<br>show that the skill of FIO-ESM v1.0 with the EAKF data assimilation scheme to predict<br>SST over the North Pacific is considerably higher than that with Projection-OI data<br>assimilation for all lead times of 1–6 months, especially in the central North Pacific where<br>the subsurface ocean temperature in the initial conditions is significantly improved by<br>EAKF data assimilation. For the Kuroshio–Oyashio extension (KOE) region, the errors<br>in the initial conditions have more rapid propagation resulting in large discrepancies<br>between simulated and observed values, which are reduced by inducing surface<br>waves into the climate model. Incorporation of realistic initial conditions and reasonable<br>physical processes into the coupled model is essential to improving seasonal prediction<br>skill. These results provide a solid basis for the development of operational seasonal<br>prediction systems for the North Pacific.</p>

scholarly journals The Role of Stochastic Forcing in Ensemble Forecasts of the 1997/98 El Niño

2009 ◽  
Vol 22 (10) ◽  
pp. 2526-2540 ◽  
Author(s):  
Li Shi ◽  
Oscar Alves ◽  
Harry H. Hendon ◽  
Guomin Wang ◽  
David Anderson
Keyword(s):  
El Niño ◽  
Initial Conditions ◽  
El Nino ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Ensemble Spread ◽  
Coupled Models ◽  
Central Pacific ◽  
Ensemble Forecasts ◽  
The Impact

Abstract The impact of stochastic intraseasonal variability on the onset of the 1997/98 El Niño was examined using a large ensemble of forecasts starting on 1 December 1996, produced using the Australian Bureau of Meteorology Predictive Ocean Atmosphere Model for Australia (POAMA) seasonal forecast coupled model. This coupled model has a reasonable simulation of El Niño and the Madden–Julian oscillation, so it provides an ideal framework for investigating the interaction between the MJO and El Niño. The experiment was designed so that the ensemble spread was simply a result of internal stochastic variability that is generated during the forecast. For the initial conditions used here, all forecasts led to warm El Niño–type conditions with the amplitude of the warming varying from 0.5° to 2.7°C in the Niño-3.4 region. All forecasts developed an MJO event during the first 4 months, indicating that perhaps the background state favored MJO development. However, the details of the MJOs that developed during December 1996–March 1997 had a significant impact on the subsequent strength of the El Niño event. In particular, the forecasts with the initial MJOs that extended farther into the central Pacific, on average, led to a stronger El Niño, with the westerly winds in the western Pacific associated with the MJO leading the development of SST and thermocline anomalies in the central and eastern Pacific. These results imply a limit to the accuracy with which the strength of El Niño can be predicted because the details of individual MJO events matter. To represent realistic uncertainty, coupled models should be able to represent the MJO, including its propagation into the central Pacific so that forecasts produce sufficient ensemble spread.

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.

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