Low Frequency Predictive Skill Despite Structural Instability and Model Error

2014 ◽  
Author(s):  
Andrew J. Majda ◽  
Rafail Abramov
Keyword(s):  
Low Frequency ◽  
Structural Instability ◽  
Model Error ◽  
Predictive Skill

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

2012 ◽  
Vol 25 (6) ◽  
pp. 1814-1826 ◽  
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.

scholarly journals An Upper Limit to Seasonal Rainfall Predictability?

2010 ◽  
Vol 23 (12) ◽  
pp. 3332-3351 ◽  
Author(s):  
Seth Westra ◽  
Ashish Sharma
Keyword(s):  
Land Surface ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Parameter Estimates ◽  
Total Precipitation ◽  
Noise Component ◽  
Nonlinear Dynamical ◽  
Surface Precipitation ◽  
Predictive Skill ◽  
Global Precipitation

Abstract The asymptotic predictability of global land surface precipitation is estimated empirically at the seasonal time scale with lead times from 0 to 12 months. Predictability is defined as the unbiased estimate of predictive skill using a given model structure assuming that all relevant predictors are included, thus representing an upper bound to the predictive skill for seasonal forecasting applications. To estimate predictability, a simple linear regression model is formulated based on the assumption that land surface precipitation variability can be divided into a component forced by low-frequency variability in the global sea surface temperature anomaly (SSTA) field and that can theoretically be predicted one or more seasons into the future, and a “weather noise” component that originates from nonlinear dynamical instabilities in the atmosphere and is not predictable beyond ~10 days. Asymptotic predictability of global precipitation was found to be 14.7% of total precipitation variance using 1900–2007 data, with only minor increases in predictability using shorter and presumably less error-prone records. This estimate was derived based on concurrent SSTA–precipitation relationships and therefore constitutes the maximum skill achievable assuming perfect forecasts of the evolution of the SSTA field. Imparting lags on the SSTA–precipitation relationship, the 3-, 6-, 9-, and 12-month predictability of global precipitation was estimated to be 7.3%, 5.4%, 4.2%, and 3.7%, respectively, demonstrating the comparative gains that can be achieved by developing improved SSTA forecasts compared to developing improved SSTA–precipitation relationships. Finally, the actual average cross-validated predictive skill was found to be 2.1% of the total precipitation variance using the full 1900–2007 dataset and was dominated by the El Niño–Southern Oscillation (ENSO) phenomenon. This indicates that there is still significant potential for increases in predictive skill through improved parameter estimates, the use of longer and/or more reliable datasets, and the use of larger spatial fields to substitute for limited temporal records.

Subseasonal Forecasts of Convectively Coupled Equatorial Waves and the MJO: Activity and Predictive Skill

2018 ◽  
Vol 146 (8) ◽  
pp. 2337-2360 ◽  
Author(s):  
Matthew A. Janiga ◽  
Carl J. Schreck ◽  
James A. Ridout ◽  
Maria Flatau ◽  
Neil P. Barton ◽  
...  
Keyword(s):  
Low Frequency ◽  
Equatorial Indian Ocean ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Kelvin Wave ◽  
Modes Of Variability ◽  
Predictive Skill ◽  
Equatorial Wave ◽  
The Tropics ◽  
Ecmwf Model

AbstractIn this study, the contribution of low-frequency (>100 days), Madden–Julian oscillation (MJO), and convectively coupled equatorial wave (CCEW) variability to the skill in predicting convection and winds in the tropics at weeks 1–3 is examined. We use subseasonal forecasts from the Navy Earth System Model (NESM); NCEP Climate Forecast System, version 2 (CFSv2); and ECMWF initialized in boreal summer 1999–2015. A technique for performing wavenumber–frequency filtering on subseasonal forecasts is introduced and applied to these datasets. This approach is better able to isolate regional variations in MJO forecast skill than traditional global MJO indices. Biases in the mean state and in the activity of the MJO and CCEWs are smallest in the ECMWF model. The NESM overestimates cloud cover as well as MJO, equatorial Rossby, and mixed Rossby–gravity/tropical depression activity over the west Pacific. The CFSv2 underestimates convectively coupled Kelvin wave activity. The predictive skill of the models at weeks 1–3 is examined by decomposing the forecasts into wavenumber–frequency signals to determine the modes of variability that contribute to forecast skill. All three models have a similar ability to simulate low-frequency variability but large differences in MJO skill are observed. The skill of the NESM and ECMWF model in simulating MJO-related OLR signals at week 2 is greatest over two regions of high MJO activity, the equatorial Indian Ocean and Maritime Continent, and the east Pacific. The MJO in the CFSv2 is too slow and too weak, which results in lower MJO skill in these regions.

scholarly journals A Multisystem View of Wintertime NAO Seasonal Predictions

2017 ◽  
Vol 30 (4) ◽  
pp. 1461-1475 ◽  
Author(s):  
Panos J. Athanasiadis ◽  
Alessio Bellucci ◽  
Adam A. Scaife ◽  
Leon Hermanson ◽  
Stefano Materia ◽  
...  
Keyword(s):  
North Atlantic ◽  
Low Frequency ◽  
Point Of View ◽  
Strong Impact ◽  
Coupled Models ◽  
Climate Anomalies ◽  
The North ◽  
Predictive Skill ◽  
Low Frequency Variability ◽  
Atmospheric Circulation Anomalies

Abstract Significant predictive skill for the mean winter North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) has been recently reported for a number of different seasonal forecasting systems. These findings are important in exploring the predictability of the natural system, but they are also important from a socioeconomic point of view, since the ability to predict the wintertime atmospheric circulation anomalies over the North Atlantic well ahead in time will have significant benefits for North American and European countries. In contrast to the tropics, for the mid latitudes the predictive skill of many forecasting systems at the seasonal time scale has been shown to be low to moderate. The recent findings are promising in this regard, suggesting that better forecasts are possible, provided that key components of the climate system are initialized realistically and the coupled models are able to simulate adequately the dominant processes and teleconnections associated with low-frequency variability. It is shown that a multisystem approach has unprecedented high predictive skill for the NAO and AO, probably largely due to increasing the ensemble size and partly due to increasing model diversity. Predicting successfully the winter mean NAO does not ensure that the respective climate anomalies are also well predicted. The NAO has a strong impact on Europe and North America, yet it only explains part of the interannual and low-frequency variability over these areas. Here it is shown with a number of different diagnostics that the high predictive skill for the NAO/AO indeed translates to more accurate predictions of temperature, surface pressure, and precipitation in the areas of influence of this teleconnection.

Estimating Central Equatorial Pacific SST Variability over the Past Millennium. Part I: Methodology and Validation

2013 ◽  
Vol 26 (7) ◽  
pp. 2302-2328 ◽  
Author(s):  
Julien Emile-Geay ◽  
Kimberly M. Cobb ◽  
Michael E. Mann ◽  
Andrew T. Wittenberg
Keyword(s):  
General Circulation ◽  
Low Frequency ◽  
General Circulation Models ◽  
Performance Assessments ◽  
The Past ◽  
Proxy Records ◽  
Sst Variability ◽  
Predictive Skill ◽  
Climate Signals ◽  
Past Millennium

Abstract Constraining the low-frequency (LF) behavior of general circulation models (GCMs) requires reliable observational estimates of LF variability. This two-part paper presents multiproxy reconstructions of Niño-3.4 sea surface temperature over the last millennium, applying two techniques [composite plus scale (CPS) and hybrid regularized expectation maximization (RegEM) truncated total least squares (TTLS)] to a network of tropical, high-resolution proxy records. This first part presents the data and methodology before evaluating their predictive skill using frozen network analysis (FNA) and pseudoproxy experiments. The FNA results suggest that about half of the Niño-3.4 variance can be reconstructed back to A.D. 1000, but they show little LF skill during certain intervals. More variance can be reconstructed in the interannual band where climate signals are strongest, but this band is affected by dating uncertainties (which are not formally addressed here). The CPS reliably estimates interannual variability, while LF fluctuations are more faithfully reconstructed with RegEM, albeit with inevitable variance loss. The RegEM approach is also tested on representative pseudoproxy networks derived from two millennium-long integrations of a coupled GCM. The pseudoproxy study confirms that reconstruction skill is significant in both the interannual and LF bands, provided that sufficient variance is exhibited in the target Niño-3.4 index. It also suggests that FNA severely underestimates LF skill, even when LF variability is strong, resulting in overly pessimistic performance assessments. The centennial-scale variance of the historical Niño-3.4 index falls somewhere between the two model simulations, suggesting that the network and methodology presented here would be able to capture the leading LF variations in Niño-3.4 for much of the past millennium, with the caveats noted above.

scholarly journals Predictive Skill and Predictable Patterns of the U.S. Seasonal Precipitation in CFSv2 Reforecasts of 60 Years (1958–2017)

2019 ◽  
Vol 32 (24) ◽  
pp. 8603-8637 ◽  
Author(s):  
Bohua Huang ◽  
Chul-Su Shin ◽  
Arun Kumar
Keyword(s):  
United States ◽  
Low Frequency ◽  
Summer Precipitation ◽  
Active Role ◽  
Winter Precipitation ◽  
Dominant Factor ◽  
Seasonal Precipitation ◽  
Continental Scale ◽  
Predictive Skill ◽  
The U.S

Abstract Analyzing a set of ensemble seasonal reforecasts for 1958–2017 using CFSv2, we evaluate the predictive skill of the U.S. seasonal mean precipitation and examine its sources of predictability. Our analysis is for each of the three periods of 1958–78, 1979–99, and 2000–17, corresponding to the positive phase of the Pacific decadal oscillation (PDO) during 1979–99 and negative ones before and after. The ensemble reforecasts at two-month lead reproduce the spatial distribution of winter precipitation trends throughout the 60 years and the continental-scale increase of summer precipitation since 2000. The predicted signal-to-noise (S/N) ratio also reveals greater predictability in the post-1979 period than during 1958–78. A maximized S/N ratio EOF analysis is applied to the ensemble seasonal precipitation predictions. In winter and spring, the most predictable patterns feature a north–south dipole throughout United States. The summer and fall patterns are dominated by the anomalies in central and southern United States, respectively. In verification with observations, the winter–spring patterns are more skillful. ENSO influences on these predictable patterns are most dominant in winter and spring, but other oceanic factors also play an active role during summer and fall. The multidecadal change of the U.S. precipitation predictability is attributable to the low-frequency modulation of the ENSO predictability and the influences of other major climate modes. PDO can be a dominant factor associated with enhanced prediction skill in 1979–99 and reduced skill in 1958–78. Since the 2000s, the forcing from the SST anomalies in the tropical North Atlantic with opposite sign to those in the tropical Pacific becomes a significant factor for the U.S. summer precipitation prediction.

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