scholarly journals Spatial Variability in Seasonal Prediction Skill of SSTs: Inherent Predictability or Forecast Errors?

2018 ◽  
Vol 31 (2) ◽  
pp. 613-621 ◽  
Author(s):  
Arun Kumar ◽  
Jieshun Zhu
Keyword(s):  
Spatial Variability ◽  
Time Scales ◽  
Seasonal Prediction ◽  
Spatial Variations ◽  
Climate System ◽  
Prediction Skill ◽  
Forecast Errors ◽  
Bjerknes Feedback ◽  
Coupled Models ◽  
Ocean Atmosphere

Seasonal prediction skill of SSTs from coupled models has considerable spatial variations. In the tropics, SST prediction skill in the tropical Pacific clearly exceeds prediction skill over the Atlantic and Indian Oceans. Such skill variations can be due to spatial variations in observing system used for forecast initializations or systematic errors in the seasonal prediction systems, or they could be a consequence of inherent properties of the coupled ocean–atmosphere system leaving a fingerprint on the spatial structure of SST predictability. Out of various alternatives, the spatial variability in SST prediction skill is argued to be a consequence of inherent characteristics of climate system. This inference is supported based on the following analyses. SST prediction skill is higher over the regions where coupled air–sea interactions (or Bjerknes feedback) are inferred to be stronger. Coupled air–sea interactions, and the longer time scales associated with them, imprint longer memory and thereby support higher SST prediction skill. The spatial variability of SST prediction skill is also consistent with differences in the ocean–atmosphere interaction regimes that distinguish between whether ocean drives the atmosphere or atmosphere drives the ocean. Regions of high SST prediction skill generally coincide with regions where ocean forces the atmosphere. Such regimes correspond to regions where oceanic variability is on longer time scales compared to regions where atmosphere forces the ocean. Such regional differences in the spatial characteristics of ocean–atmosphere interactions, in turn, also govern the spatial variations in SST skill, making spatial variations in skill an intrinsic property of the climate system and not an artifact of the observing system or model biases.

scholarly journals PNA Predictability at Various Time Scales

2013 ◽  
Vol 26 (22) ◽  
pp. 9090-9114 ◽  
Author(s):  
Waqar Younas ◽  
Youmin Tang
Keyword(s):  
Time Scales ◽  
Initial Conditions ◽  
Prediction Skill ◽  
Multiple Model ◽  
Coupled Models ◽  
Potential Predictability ◽  
Effective Measure ◽  
Dispersion Component ◽  
Significant Difference ◽  
Better Than

Abstract In this study, the predictability of the Pacific–North American (PNA) pattern is evaluated on time scales from days to months using state-of-the-art dynamical multiple-model ensembles including the Canadian Historical Forecast Project (HFP2) ensemble, the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) ensemble, and the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES). Some interesting findings in this study include (i) multiple-model ensemble (MME) skill was better than most of the individual models; (ii) both actual prediction skill and potential predictability increased as the averaging time scale increased from days to months; (iii) there is no significant difference in actual skill between coupled and uncoupled models, in contrast with the potential predictability where coupled models performed better than uncoupled models; (iv) relative entropy (REA) is an effective measure in characterizing the potential predictability of individual prediction, whereas the mutual information (MI) is a reliable indicator of overall prediction skill; and (v) compared with conventional potential predictability measures of the signal-to-noise ratio, the MI-based measures characterized more potential predictability when the ensemble spread varied over initial conditions. Further analysis found that the signal component dominated the dispersion component in REA for PNA potential predictability from days to seasons. Also, the PNA predictability is highly related to the signal of the tropical sea surface temperature (SST), and SST–PNA correlation patterns resemble the typical ENSO structure, suggesting that ENSO is the main source of PNA seasonal predictability. The predictable component analysis (PrCA) of atmospheric variability further confirmed the above conclusion; that is, PNA is one of the most predictable patterns in the climate variability over the Northern Hemisphere, which originates mainly from the ENSO forcing.

scholarly journals Deep learning for bias correction of MJO prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
H. Kim ◽  
Y. G. Ham ◽  
Y. S. Joo ◽  
S. W. Son
Keyword(s):  
Deep Learning ◽  
Bias Correction ◽  
Numerical Models ◽  
Weather Prediction ◽  
Primary Source ◽  
Correction Method ◽  
Prediction Skill ◽  
Convective System ◽  
Forecast Errors ◽  
Potential Predictability

AbstractProducing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

scholarly journals Frequency-Domain Analysis of the Energy Budget in an Idealized Coupled Ocean–Atmosphere Model

2020 ◽  
Vol 33 (2) ◽  
pp. 707-726 ◽  
Author(s):  
Paige E. Martin ◽  
Brian K. Arbic ◽  
Andrew McC. Hogg ◽  
Andrew E. Kiss ◽  
James R. Munroe ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Time Scales ◽  
Energy Budget ◽  
Western Boundary Current ◽  
Spectral Energy ◽  
Western Boundary ◽  
Intrinsic Variability ◽  
Boundary Current ◽  
Ocean Atmosphere ◽  
Atmosphere Model

AbstractClimate variability is investigated by identifying the energy sources and sinks in an idealized, coupled, ocean–atmosphere model, tuned to mimic the North Atlantic region. The spectral energy budget is calculated in the frequency domain to determine the processes that either deposit energy into or extract energy from each fluid, over time scales from one day up to 100 years. Nonlinear advection of kinetic energy is found to be the dominant source of low-frequency variability in both the ocean and the atmosphere, albeit in differing layers in each fluid. To understand the spatial patterns of the spectral energy budget, spatial maps of certain terms in the spectral energy budget are plotted, averaged over various frequency bands. These maps reveal three dynamically distinct regions: along the western boundary, the western boundary current separation, and the remainder of the domain. The western boundary current separation is found to be a preferred region to energize oceanic variability across a broad range of time scales (from monthly to decadal), while the western boundary itself acts as the dominant sink of energy in the domain at time scales longer than 50 days. This study paves the way for future work, using the same spectral methods, to address the question of forced versus intrinsic variability in a coupled climate system.

scholarly journals Lagged Ensembles, Forecast Configuration, and Seasonal Predictions

2013 ◽  
Vol 141 (10) ◽  
pp. 3477-3497 ◽  
Author(s):  
Mingyue Chen ◽  
Wanqiu Wang ◽  
Arun Kumar
Keyword(s):  
Lead Time ◽  
Geographical Location ◽  
Seasonal Prediction ◽  
Skill Score ◽  
Negative Influence ◽  
Positive Influence ◽  
Prediction Skill ◽  
Ensemble Size ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts

Abstract An analysis of lagged ensemble seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), is presented. The focus of the analysis is on the construction of lagged ensemble forecasts with increasing lead time (thus allowing use of larger ensemble sizes) and its influence on seasonal prediction skill. Predictions of seasonal means of sea surface temperature (SST), 200-hPa height (z200), precipitation, and 2-m air temperature (T2m) over land are analyzed. Measures of prediction skill include deterministic (anomaly correlation and mean square error) and probabilistic [rank probability skill score (RPSS)]. The results show that for a fixed lead time, and as one would expect, the skill of seasonal forecast improves as the ensemble size increases, while for a fixed ensemble size the forecast skill decreases as the lead time becomes longer. However, when a forecast is based on a lagged ensemble, there exists an optimal lagged ensemble time (OLET) when positive influence of increasing ensemble size and negative influence due to an increasing lead time result in a maximum in seasonal prediction skill. The OLET is shown to depend on the geographical location and variable. For precipitation and T2m, OLET is relatively longer and skill gain is larger than that for SST and tropical z200. OLET is also dependent on the skill measure with RPSS having the longest OLET. Results of this analysis will be useful in providing guidelines on the design and understanding relative merits for different configuration of seasonal prediction systems.

scholarly journals An Analysis of the Temporal Evolution of ENSO Prediction Skill in the Context of the Equatorial Pacific Ocean Observing System

2015 ◽  
Vol 143 (8) ◽  
pp. 3204-3213 ◽  
Author(s):  
Arun Kumar ◽  
Mingyue Chen ◽  
Yan Xue ◽  
David Behringer
Keyword(s):  
Pacific Ocean ◽  
Time Evolution ◽  
Tropical Pacific ◽  
Sea Surface ◽  
Prediction Skill ◽  
Steady Increase ◽  
Coupled Models ◽  
Enso Prediction ◽  
Subsurface Ocean ◽  
Ocean Observations

Abstract Subsurface ocean observations in the equatorial tropical Pacific Ocean dramatically increased after the 1990s because of the completion of the TAO moored array and a steady increase in Argo floats. In this analysis the question explored is whether a steady increase in ocean observations can be discerned in improvements in skill of predicting sea surface temperature (SST) variability associated with El Niño–Southern Oscillation (ENSO)? The analysis is based on the time evolution of skill of sea surface temperatures in the equatorial tropical Pacific since 1982 based on a seasonal prediction system. It is found that for forecasts up to a 6-month lead time, a clear fingerprint of increases in subsurface ocean observations is not readily apparent in the time evolution of prediction skill that is dominated much more by the signal-to-noise consideration of SSTs to be predicted. Finding no clear relationship between an increase in ocean observations and prediction skill of SSTs, various possibilities for why it may be so are discussed. This discussion is to motivate further exploration on the question of the tropical Pacific observing system, its influence on the skill of ENSO prediction, and the capabilities of the current generation of coupled models and ocean data assimilation systems to take advantage of ocean observations.

Seasonal-to-Decadal Predictability and Prediction of North American Climate—The Atlantic Influence

2006 ◽  
Vol 19 (23) ◽  
pp. 6005-6024 ◽  
Author(s):  
H. M. Van den Dool ◽  
Peitao Peng ◽  
Åke Johansson ◽  
Muthuvel Chelliah ◽  
Amir Shabbar ◽  
...  
Keyword(s):  
North American ◽  
Seasonal Prediction ◽  
Atmospheric Model ◽  
The United States ◽  
Seasonal Forecast ◽  
Ocean Model ◽  
Prediction Skill ◽  
Basic Material ◽  
Atlantic Sector ◽  
The Impact

Abstract The question of the impact of the Atlantic on North American (NA) seasonal prediction skill and predictability is examined. Basic material is collected from the literature, a review of seasonal forecast procedures in Canada and the United States, and some fresh calculations using the NCEP–NCAR reanalysis data. The general impression is one of low predictability (due to the Atlantic) for seasonal mean surface temperature and precipitation over NA. Predictability may be slightly better in the Caribbean and the (sub)tropical Americas, even for precipitation. The NAO is widely seen as an agent making the Atlantic influence felt in NA. While the NAO is well established in most months, its prediction skill is limited. Year-round evidence for an equatorially displaced version of the NAO (named ED_NAO) carrying a good fraction of the variance is also found. In general the predictability from the Pacific is thought to dominate over that from the Atlantic sector, which explains the minimal number of reported Atmospheric Model Intercomparison Project (AMIP) runs that explore Atlantic-only impacts. Caveats are noted as to the question of the influence of a single predictor in a nonlinear environment with many predictors. Skill of a new one-tier global coupled atmosphere–ocean model system at NCEP is reviewed; limited skill is found in midlatitudes and there is modest predictability to look forward to. There are several signs of enthusiasm in the community about using “trends” (low-frequency variations): (a) seasonal forecast tools include persistence of last 10 years’ averaged anomaly (relative to the official 30-yr climatology), (b) hurricane forecasts are based largely on recognizing a global multidecadal mode (which is similar to an Atlantic trend mode in SST), and (c) two recent papers, one empirical and one modeling, giving equal roles to the (North) Pacific and Atlantic in “explaining” variations in drought frequency over NA on a 20 yr or longer time scale during the twentieth century.

scholarly journals Late quaternary temperature variability described as abrupt transitions on a 1/<i>f</i> noise background

2015 ◽  
Vol 6 (2) ◽  
pp. 2323-2337
Author(s):  
M. Rypdal ◽  
K. Rypdal
Keyword(s):  
Time Scales ◽  
Late Quaternary ◽  
Scaling Law ◽  
Temperature Variability ◽  
Climate System ◽  
Scaling Analysis ◽  
Noise Background ◽  
Quaternary Climate ◽  
Last Glaciation ◽  
Climate Noise

Abstract. We show that in order to have a scaling description of the climate system that is not inherently non-stationary, the rapid shifts between stadial and interstadial conditions during the last glaciation cannot be included in the scaling law. The same is true for the shifts between the glacial and interglacial states in the quaternary climate. When these events are omitted from a scaling analysis we find that the climate noise is consistent with a 1/f law on time scales from months to 105 years.

Influence of the QBO on MJO prediction skill in the subseasonal-to-seasonal prediction models

2019 ◽  
Vol 53 (3-4) ◽  
pp. 1681-1695 ◽  
Author(s):  
Yuna Lim ◽  
Seok-Woo Son ◽  
Andrew G. Marshall ◽  
Harry H. Hendon ◽  
Kyong-Hwan Seo
Keyword(s):  
Prediction Models ◽  
Seasonal Prediction ◽  
Prediction Skill

scholarly journals Southern Ocean Wind Stress in CMIP5 Models: Role of Wind Fluctuations

2020 ◽  
Vol 33 (4) ◽  
pp. 1209-1226 ◽  
Author(s):  
Xia Lin ◽  
Xiaoming Zhai ◽  
Zhaomin Wang ◽  
David R. Munday
Keyword(s):  
Southern Ocean ◽  
Time Scales ◽  
Wind Stress ◽  
Surface Wind ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Coupled Models ◽  
Surface Wind Stress ◽  
Stress Changes ◽  
The Mean

AbstractThe Southern Ocean (SO) surface wind stress is a major atmospheric forcing for driving the Antarctic Circumpolar Current and the global overturning circulation. Here the effects of wind fluctuations at different time scales on SO wind stress in 18 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are investigated. It is found that including wind fluctuations, especially on time scales associated with synoptic storms, in the stress calculation strongly enhances the mean strength, modulates the seasonal cycle, and significantly amplifies the trends of SO wind stress. In 11 out of the 18 CMIP5 models, the SO wind stress has strengthened significantly over the period of 1960–2005. Among them, the strengthening trend of SO wind stress in one CMIP5 model is due to the increase in the intensity of wind fluctuations, while in all the other 10 models the strengthening trend is due to the increasing strength of the mean westerly wind. These discrepancies in SO wind stress trend in CMIP5 models may explain some of the diverging behaviors in the model-simulated SO circulation. Our results suggest that to reduce the uncertainty in SO responses to wind stress changes in the coupled models, both the mean wind and wind fluctuations need to be better simulated.

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