238 research outputs found
Oceanic stochastic parametrizations in a seasonal forecast system
We study the impact of three stochastic parametrizations in the ocean
component of a coupled model, on forecast reliability over seasonal timescales.
The relative impacts of these schemes upon the ocean mean state and ensemble
spread are analyzed. The oceanic variability induced by the atmospheric forcing
of the coupled system is, in most regions, the major source of ensemble spread.
The largest impact on spread and bias came from the Stochastically Perturbed
Parametrization Tendency (SPPT) scheme - which has proven particularly
effective in the atmosphere. The key regions affected are eddy-active regions,
namely the western boundary currents and the Southern Ocean. However, unlike
its impact in the atmosphere, SPPT in the ocean did not result in a significant
decrease in forecast error. Whilst there are good grounds for implementing
stochastic schemes in ocean models, our results suggest that they will have to
be more sophisticated. Some suggestions for next-generation stochastic schemes
are made.Comment: 24 pages, 3 figure
MultiâDecadal Skill Variability in Predicting the Spatial Patterns of ENSO Events
Seasonal hindcasts have previously been demonstrated to show multiâdecadal variability in skill across the twentieth century in indices describing ElâNiño Southern Oscillation (ENSO), which drives global seasonal predictability. Here, we analyze the skill of predicting ENSO events' magnitude and spatial pattern, in the CSFâ20C coupled seasonal hindcasts in 1901â2010. We find minima in the skill of predicting the first (in 1930â1950) and second (in 1940â1960) principal components of seaâsurface temperature (SST) in the tropical Pacific. This minimum is also present in the spatial correlation of SSTs, in 1930â1960. The skill reduction is explained by lower ENSO magnitude and variance in 1930â1960, as well as decreased SST persistence. The SST skill minima project onto surface winds, leading to worse predictions in coupled hindcasts compared to hindcasts using prescribed SSTs. Questions remain about the offset between the first and second principal components' skill minima, and how the skill minima impact the extraâtropics
Oceanic stochastic parametrizations in a seasonal forecast system
We study the impact of three stochastic parametrizations in the ocean
component of a coupled model, on forecast reliability over seasonal timescales.
The relative impacts of these schemes upon the ocean mean state and ensemble
spread are analyzed. The oceanic variability induced by the atmospheric forcing
of the coupled system is, in most regions, the major source of ensemble spread.
The largest impact on spread and bias came from the Stochastically Perturbed
Parametrization Tendency (SPPT) scheme - which has proven particularly
effective in the atmosphere. The key regions affected are eddy-active regions,
namely the western boundary currents and the Southern Ocean. However, unlike
its impact in the atmosphere, SPPT in the ocean did not result in a significant
decrease in forecast error. Whilst there are good grounds for implementing
stochastic schemes in ocean models, our results suggest that they will have to
be more sophisticated. Some suggestions for next-generation stochastic schemes
are made.Comment: 24 pages, 3 figure
Recommended from our members
Improved seasonal prediction of the hot summer of 2003 over Europe through better representation of uncertainty in the land surface
Methods to explicitly represent uncertainties in weather and climate models have been developed and refined over the past decade, and have reduced biases and improved forecast skill when implemented in the atmospheric component of models. These methods have not yet been applied to the land surface component of models. Since the land surface is strongly coupled to the atmospheric state at certain times and in certain places (such as the European summer of 2003), improvements in the representation of land surface uncertainty may potentially lead to improvements in atmospheric forecasts for such events.
Here we analyse seasonal retrospective forecasts for 1981â2012 performed with the European Centre for Medium-Range Weather Forecastsâ (ECMWF) coupled ensemble forecast model. We consider two methods of incorporating uncertainty into the land surface model (H-TESSEL): stochastic perturbation of tendencies, and static perturbation of key soil parameters.
We find that the perturbed parameter approach considerably improves the forecast of extreme air temperature for summer 2003, through better representation of negative soil moisture anomalies and upward sensible heat flux. Averaged across all the reforecasts the perturbed parameter experiment shows relatively little impact on the mean bias, suggesting perturbations of at least this magnitude can be applied to the land surface without any degradation of model climate. There is also little impact on skill averaged across all reforecasts and some evidence of overdispersion for soil moisture.
The stochastic tendency experiments show a large overdispersion for the soil temperature fields, indicating that the perturbation here is too strong. There is also some indication that the forecast of the 2003 warm event is improved for the stochastic experiments, however the improvement is not as large as observed for the perturbed parameter experiment
Understanding the Intermittency of the Wintertime North Atlantic Oscillation and East Atlantic Pattern Seasonal Forecast Skill in the Copernicus C3S MultiâModel Ensemble
The wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are the two leading modes of North Atlantic pressure variability and have a substantial impact on winter weather in Europe. The yearâtoâyear contributions to multiâmodel seasonal forecast skill in the Copernicus C3S ensemble of seven prediction systems are assessed for the wintertime NAO and EA, and wellâforecast and poorlyâforecast years are identified. Years with high NAO predictability are associated with substantial tropical forcing, generally from the El Niño Southern Oscillation (ENSO), while poor forecasts of the NAO occur when ENSO forcing is weak. Wellâforecast EA winters also generally occurred when there was substantial tropical forcing, although the relationship was less robust than for the NAO. These results support previous findings of the impacts of tropical forcing on the North Atlantic and show this is important from a multiâmodel seasonal forecasting perspective
Revisiting the identification of wintertime atmospheric circulation regimes in the Euro-Atlantic sector
Atmospheric circulation is often clustered in so-called circulation regimes, which are persistent and recurrent patterns. For the Euro-Atlantic sector in winter, most studies identify four regimes: the Atlantic Ridge, the Scandinavian Blocking and the two phases of the North Atlantic Oscillation. These results are obtained by applying k-means clustering to the first several empirical orthogonal functions (EOFs) of geopotential height data. Studying the observed circulation in reanalysis data, it is found that when the full field data is used for the k-means cluster analysis instead of the EOFs, the optimal number of clusters is no longer four but six. The two extra regimes that are found are the opposites of the Atlantic Ridge and Scandinavian Blocking, meaning they have a low-pressure area roughly where the original regimes have a high-pressure area. This introduces an appealing symmetry in the clustering result. Incorporating a weak persistence constraint in the clustering procedure is found to lead to a longer duration of regimes, extending beyond the synoptic timescale, without changing their occurrence rates. This is in contrast to the commonly-used application of a time-filter to the data before the clustering is executed, which, while increasing the persistence, changes the occurrence rates of the regimes. We conclude that applying a persistence constraint within the clustering procedure is a superior way of stabilizing the clustering results than low-pass filtering the data
ENSEMBLES: a new multi-model ensemble for seasonal-to-annual predictions: Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs
A new 46-year hindcast dataset for seasonal-to-annual ensemble predictions has been created using a multi-model ensemble of 5 state-of-the-art coupled atmosphere-ocean circulation models. The multi-model outperforms any of the single-models in forecasting tropical Pacific SSTs because of reduced RMS errors and enhanced ensemble dispersion at all lead-times. Systematic errors are considerably reduced over the previous generation (DEMETER). Probabilistic skill scores show higher skill for the new multi-model ensemble than for DEMETER in the 4â6 month forecast range. However, substantially improved models would be required to achieve strongly statistical significant skill increases. The combination of ENSEMBLES and DEMETER into a grand multi-model ensemble does not improve the forecast skill further. Annual-range hindcasts show anomaly correlation skill of âŒ0.5 up to 14 months ahead. A wide range of output from the multi-model simulations is becoming publicly available and the international community is invited to explore the full scientific potential of these data
OpenIFS@home version 1: a citizen science project for ensemble weather and climate forecasting
Weather forecasts rely heavily on general circulation models of
the atmosphere and other components of the Earth system. National
meteorological and hydrological services and intergovernmental
organizations, such as the European Centre for Medium-Range Weather
Forecasts (ECMWF), provide routine operational forecasts on a range of
spatio-temporal scales by running these models at high resolution on
state-of-the-art high-performance computing systems. Such operational
forecasts are very demanding in terms of computing resources. To facilitate
the use of a weather forecast model for research and training purposes
outside the operational environment, ECMWF provides a portable version of
its numerical weather forecast model, OpenIFS, for use by universities and
other research institutes on their own computing systems.
In this paper, we describe a new project (OpenIFS@home) that combines
OpenIFS with a citizen science approach to involve the general public in
helping conduct scientific experiments. Volunteers from across the world can
run OpenIFS@home on their computers at home, and the results of these
simulations can be combined into large forecast ensembles. The
infrastructure of such distributed computing experiments is based on our
experience and expertise with the climateprediction.net (https://www.climateprediction.net/, last access: 1 June 2021) and
weather@home systems.
In order to validate this first use of OpenIFS in a volunteer computing
framework, we present results from ensembles of forecast simulations of
Tropical Cyclone Karl from September 2016 studied during the NAWDEX field
campaign. This cyclone underwent extratropical transition and intensified in
mid-latitudes to give rise to an intense jet streak near Scotland and heavy
rainfall over Norway. For the validation we use a 2000-member
ensemble of OpenIFS run on the OpenIFS@home volunteer framework and a
smaller ensemble of the size of operational forecasts using ECMWF's forecast
model in 2016 run on the ECMWF supercomputer with the same horizontal
resolution as OpenIFS@home. We present ensemble statistics that illustrate
the reliability and accuracy of the OpenIFS@home forecasts and
discuss the use of large ensembles in the context of forecasting extreme
events.</p
Recommended from our members
An intercomparison of skill and overconfidence/underconfidence of the wintertime North Atlantic Oscillation in multimodel seasonal forecasts
Recent studies of individual seasonal forecast systems have shown that the wintertime North Atlantic Oscillation (NAO) can be skillfully forecast. However, it has also been suggested that these skillful forecasts tend to be underconfident, meaning that there is too high a proportion of unpredictable noise in the forecasts. We assess the skill and overconfidence/underconfidence of the seasonal forecast systems contributing to the EUROpean Seasonal to Interannual Prediction (EUROSIP) multimodel ensemble system. Five of the seven systems studied have significant skill for forecasting the wintertime NAO at 2â to 4âmonth lead times. Four of these skillful systems are underconfident for forecasting the NAO. A multimodel ensemble (ensemble size 126 members) is both skillful and clearly underconfident. Underconfidence becomes more pronounced as the ensemble size increases. Certain years in the hindcast period are well forecast by all or most models. This implies that common teleconnections and drivers of the NAO are being captured by the EUROSIP seasonal forecasts
- âŠ