On the predictive skill of morphodynamic models for onshore sandbar migration

AbstractMesoscale eddies dominate energetics of the ocean, modify mass, heat and freshwater transport and primary production in the upper ocean. However, the forecast skill horizon for ocean mesoscales in current operational models is shorter than 10 days: eddy-resolving ocean models, with horizontal resolution finer than 10 km in mid-latitudes, represent mesoscale dynamics, but mesoscale initial conditions are hard to constrain with available observations. Here we analyze a suite of ocean model simulations at high (1/25°) and lower (1/12.5°) resolution and compare with an ensemble of lower-resolution simulations. We show that the ensemble forecast significantly extends the predictability of the ocean mesoscales to between 20 and 40 days. We find that the lack of predictive skill in data assimilative deterministic ocean models is due to high uncertainty in the initial location and forecast of mesoscale features. Ensemble simulations account for this uncertainty and filter-out unconstrained scales. We suggest that advancements in ensemble analysis and forecasting should complement the current focus on high-resolution modeling of the ocean.

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Quantifying the Predictive Skill in Long-Range Forecasting. Part II: Model Error in Coarse-Grained Markov Models with Application to Ocean-Circulation Regimes

Journal of Climate ◽

10.1175/jcli-d-11-00110.1 ◽

2012 ◽

Vol 25 (6) ◽

pp. 1814-1826 ◽

Cited By ~ 19

Author(s):

Dimitrios Giannakis ◽

Andrew J. Majda

Keyword(s):

Long Range ◽

Ocean Circulation ◽

Climate Models ◽

Markov Models ◽

Low Frequency ◽

Model Error ◽

Ocean Model ◽

Coarse Grained ◽

Predictive Skill ◽

Relaxation To Equilibrium

Abstract An information-theoretic framework is developed to assess the predictive skill and model error in imperfect climate models for long-range forecasting. Here, of key importance is a climate equilibrium consistency test for detecting false predictive skill, as well as an analogous criterion describing model error during relaxation to equilibrium. Climate equilibrium consistency enforces the requirement that long-range forecasting models should reproduce the climatology of prediction observables with high fidelity. If a model meets both climate consistency and the analogous criterion describing model error during relaxation to equilibrium, then relative entropy can be used as an unbiased superensemble measure of the model’s skill in long-range coarse-grained forecasts. As an application, the authors investigate the error in modeling regime transitions in a 1.5-layer ocean model as a Markov process and identify models that are strongly persistent but their predictive skill is false. The general techniques developed here are also useful for estimating predictive skill with model error for Markov models of low-frequency atmospheric regimes.

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Analog Downscaling of Seasonal Rainfall Forecasts in the Murray Darling Basin

Monthly Weather Review ◽

10.1175/mwr-d-12-00098.1 ◽

2013 ◽

Vol 141 (3) ◽

pp. 1099-1117 ◽

Cited By ~ 18

Author(s):

Andrew Charles ◽

Bertrand Timbal ◽

Elodie Fernandez ◽

Harry Hendon

Keyword(s):

General Circulation ◽

Large Scale ◽

Seasonal Prediction ◽

General Circulation Models ◽

Meridional Wind ◽

Seasonal Forecasts ◽

Local Climate ◽

Continental Scale ◽

Predictive Skill ◽

Murray Darling Basin

Abstract Seasonal predictions based on coupled atmosphere–ocean general circulation models (GCMs) provide useful predictions of large-scale circulation but lack the conditioning on topography required for locally relevant prediction. In this study a statistical downscaling model based on meteorological analogs was applied to continental-scale GCM-based seasonal forecasts and high quality historical site observations to generate a set of downscaled precipitation hindcasts at 160 sites in the South Murray Darling Basin region of Australia. Large-scale fields from the Predictive Ocean–Atmosphere Model for Australia (POAMA) 1.5b GCM-based seasonal prediction system are used for analog selection. Correlation analysis indicates modest levels of predictability in the target region for the selected predictor fields. A single best-match analog was found using model sea level pressure, meridional wind, and rainfall fields, with the procedure applied to 3-month-long reforecasts, initialized on the first day of each month from 1980 to 2006, for each model day of 10 ensemble members. Assessment of the total accumulated rainfall and number of rainy days in the 3-month reforecasts shows that the downscaling procedure corrects the local climate variability with no mean effect on predictive skill, resulting in a smaller magnitude error. The amount of total rainfall and number of rain days in the downscaled output is significantly improved over the direct GCM output as measured by the difference in median and tercile thresholds between station observations and downscaled rainfall. Confidence in the downscaled output is enhanced by strong consistency between the large-scale mean of the downscaled and direct GCM precipitation.

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NAO predictability from external forcing in the late 20th century

npj Climate and Atmospheric Science ◽

10.1038/s41612-021-00177-8 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Jeremy M. Klavans ◽

Mark A. Cane ◽

Amy C. Clement ◽

Lisa N. Murphy

Keyword(s):

North Atlantic ◽

Radiative Forcing ◽

Climate Models ◽

North Atlantic Ocean ◽

Signal To Noise Ratio ◽

Prediction System ◽

Signal To Noise ◽

Late 20Th Century ◽

The North ◽

Predictive Skill

AbstractThe North Atlantic Oscillation (NAO) is predictable in climate models at near-decadal timescales. Predictive skill derives from ocean initialization, which can capture variability internal to the climate system, and from external radiative forcing. Herein, we show that predictive skill for the NAO in a very large uninitialized multi-model ensemble is commensurate with previously reported skill from a state-of-the-art initialized prediction system. The uninitialized ensemble and initialized prediction system produce similar levels of skill for northern European precipitation and North Atlantic SSTs. Identifying these predictable components becomes possible in a very large ensemble, confirming the erroneously low signal-to-noise ratio previously identified in both initialized and uninitialized climate models. Though the results here imply that external radiative forcing is a major source of predictive skill for the NAO, they also indicate that ocean initialization may be important for particular NAO events (the mid-1990s strong positive NAO), and, as previously suggested, in certain ocean regions such as the subpolar North Atlantic ocean. Overall, we suggest that improving climate models’ response to external radiative forcing may help resolve the known signal-to-noise error in climate models.

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Propagation of Thermohaline Anomalies and their predictive potential in the Northern North Atlantic

10.5194/egusphere-egu21-15092 ◽

2021 ◽

Author(s):

Helene R. Langehaug ◽

Pablo Ortega ◽

Francois Counillon ◽

Daniela Matei ◽

Elizabeth Maroon ◽

...

Keyword(s):

Time Scales ◽

North Atlantic ◽

Atlantic Water ◽

The Arctic ◽

Lead Times ◽

Band Pass Filter ◽

Pass Filter ◽

Predictive Skill ◽

Prediction Systems ◽

Water Pathway

In this study we assess to what extent seven different dynamical prediction systems can retrospectively predict the winter sea surface temperature (SST) in the subpolar North Atlantic and the Nordic Seas in the time period 1970-2005. We focus in particular on the region where warm water flows poleward, i.e., the Atlantic water pathway, and on interannual-to-decadal time scales. To better understand why dynamical prediction systems have predictive skill or lack thereof, we confront them with a mechanism identified from observations &#8211; propagation of oceanic anomalies from low to high latitudes &#8211; on different forecast lead times. This observed mechanism shows that warm and cold anomalies propagate along the Atlantic water pathway within a certain time frame. A key result from this study is that most models have difficulty representing this mechanism, resulting in an overall poor prediction skill after 1-2 years lead times (after applying a band-pass filter to focus on interannual-to-decadal time scales). There is a link, although not very strong, between predictive skill and the representation of the SST propagation. Observational studies demonstrate predictability several years in advance in this region, thus suggesting a great potential for improvement of dynamical climate predictions by resolving the causes for the misrepresentation of the oceanic link. Inter model differences in simulating surface velocities along the Atlantic water pathway suggest that realistic velocities are important to better circulate anomalies poleward, and hence, increase predictive skill on interannual-to-decadal time scales in the oceanic gateway to the Arctic.

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How prediction statistics can help us cope when we are shaken, scared and irrational

10.5194/egusphere-egu21-15219 ◽

2021 ◽

Author(s):

Yavor Kamer ◽

Shyam Nandan ◽

Stefan Hiemer ◽

Guy Ouillon ◽

Didier Sornette

Keyword(s):

Earthquake Prediction ◽

Information Technologies ◽

Predictive Performance ◽

Earthquake Forecasting ◽

Skill Assessment ◽

Surveillance Network ◽

The Public ◽

Predictive Skill ◽

Earthquake Predictability ◽

Set Up

Nature is scary. You can be sitting at your home and next thing you know you are trapped under the ruble of your own house or sucked into a sinkhole. For millions of years we have been the figurines of this precarious scene and we have found our own ways of dealing with the anxiety. It is natural that we create and consume prophecies, conspiracies and false predictions. Information technologies amplify not only our rational but also irrational deeds. Social media algorithms, tuned to maximize attention, make sure that misinformation spreads much faster than its counterpart.What can we do to minimize the adverse effects of misinformation, especially in the case of earthquakes? One option could be to designate one authoritative institute, set up a big surveillance network and cancel or ban every source of misinformation before it spreads. This might have worked a few centuries ago but not in this day and age. Instead we propose a more inclusive option: embrace all voices and channel them into an actual, prospective earthquake prediction platform (Kamer et al. 2020). The platform is powered by a global state-of-the-art statistical earthquake forecasting model that provides near real-time earthquake occurrence probabilities anywhere on the globe (Nandan et al. 2020). Using this model as a benchmark in statistical metrics specifically tailored to the prediction problem, we are able to distill all these voices and quantify the essence of predictive skill. This approach has several advantages. Rather than trying to silence or denounce, we listen and evaluate each claim and report the predictive skill of the source. We engage the public and allow them to take part in a scientific experiment that will increase their risk awareness. We effectively demonstrate that anybody with an internet connected device can make an earthquake prediction, but that it is not so trivial to achieve skillful predictive performance.Here we shall present initial results from our global earthquake prediction experiment that we have been conducting on www.richterx.com for the past two years, yielding more than 10,000 predictions. These results will hopefully demystify the act of predicting an earthquake in the eyes of the public, and next time someone forwards a prediction message it would arouse more scrutiny than panic or distaste. Nandan, S., Kamer, Y., Ouillon, G., Hiemer, S., Sornette, D. (2020). Global models for short-term earthquake forecasting and predictive skill assessment. European Physical Journal ST. doi: 10.1140/epjst/e2020-000259-3 Kamer, Y., Nandan, S., Ouillon, G., Hiemer, S., Sornette, D. (2020). Democratizing earthquake predictability research: introducing the RichterX platform. European Physical Journal ST. doi: 10.1140/epjst/e2020-000260-2&#160;

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Assessing ENSO Simulations and Predictions Using Adjoint Ocean State Estimation

Journal of Climate ◽

10.1175/3211.1 ◽

2004 ◽

Vol 17 (22) ◽

pp. 4301-4315 ◽

Cited By ~ 12

Author(s):

Dietmar Dommenget ◽

Detlef Stammer

Keyword(s):

State Estimation ◽

Positive Impact ◽

Coupled Model ◽

Estimation Procedure ◽

Time Interval ◽

Seasonal Forecasts ◽

Predictive Skill ◽

Sst Anomaly ◽

Ocean State Estimation

Abstract Simulations and seasonal forecasts of tropical Pacific SST and subsurface fields that are based on the global Consortium for Estimating the Circulation and Climate of the Ocean (ECCO) ocean-state estimation procedure are investigated. As compared to similar results from a traditional ENSO simulation and forecast procedure, the hindcast of the constrained ocean state is significantly closer to observed surface and subsurface conditions. The skill of the 12-month lead SST forecast in the equatorial Pacific is comparable in both approaches. The optimization appears to have better skill in the SST anomaly correlations, suggesting that the initial ocean conditions and forcing corrections calculated by the ocean-state estimation do have a positive impact on the predictive skill. However, the optimized forecast skill is currently limited by the low quality of the statistical atmosphere. Progress is expected from optimizing a coupled model over a longer time interval with the coupling statistics being part of the control vector.

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