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Skill for initialized decadal predictions for atmospheric and terrestrial variability is posited to reside in successful prediction of sea surface temperatures (SSTs) associated with the low-frequency modes of coupled ocean-atmosp...
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Skill for initialized decadal predictions for atmospheric and terrestrial variability is posited to reside in successful prediction of sea surface temperatures (SSTs) associated with the low-frequency modes of coupled ocean-atmosphere variability, for example, Pacific Decadal Oscillation (PDO) or Atlantic Multi-decadal Oscillation (AMO). So far, assessments of the skill of atmospheric and terrestrial variability in decadal predictions, however, have not been encouraging. Similarly, in the context of seasonal climate variability, teleconnections between SSTs associated with PDO and AMO and terrestrial climate have also been noted, but the same SST information used in predictive mode has failed to demonstrate convincing gains in skill. Are these results an artifact of model biases, or more a consequence of some fundamental property of coupled evolution of ocean-atmosphere system in extratropical latitudes, and the manner in which extratropical SST anomalies modulate (or constrain) atmospheric variability? Based on revisiting an analysis of a simple model that replicates the essential characteristics of coupled ocean-atmosphere interaction in extratropical latitudes, it is demonstrated that lack of additional skill in predicting atmospheric and terrestrial variability is more a consequence of fundamental characteristics of coupled evolution of ocean-atmosphere system. The results based on simple models are also substantiated following an analysis of a set of seasonal hind-casts with a fully coupled model.
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Forecasts for extremes in short-term climate (monthly means) are examined to understand the current prediction capability and potential predictability. This study focuses on 2-m surface temperature and precipitation extremes over ...
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Forecasts for extremes in short-term climate (monthly means) are examined to understand the current prediction capability and potential predictability. This study focuses on 2-m surface temperature and precipitation extremes over North and South America, and sea surface temperature extremes in the Nino-3.4 and Atlantic hurricane main development regions, using the Climate Forecast System (CFS) global climate model, for the period of 1982-2010. The primary skill measures employed are the anomaly correlation (AC) and root-mean-square error (RMSE). The success rate of forecasts is also assessed using contingency tables. The AC, a signal-to-noise skill measure, is routinely higher for extremes in short-term climate than those when all forecasts are considered. While the RMSE for extremes also rises, especially when skill is inherently low, it is found that the signal rises faster than the noise. Permutation tests confirm that this is not simply an effect of reduced sample size. Both 2-m temperature and precipitation forecasts have higher anomaly correlations in the area of South America than North America; credible skill in precipitation is very low over South America and absent over North America, even for extremes. Anomaly correlations for SST are very high in the Nino-3.4 region, especially for extremes, and moderate to high in the Atlantic hurricane main development region. Prediction skill for forecast extremes is similar to skill for observed extremes. Assessment of the potential predictability under perfect-model assumptions shows that predictability and prediction skill have very similar space-time dependence. While prediction skill is higher in CFS version 2 than in CFS version 1, the potential predictability is not.Digital Object Identifier http://dx.doi.org/10.1175/JCLI-D-12-00177.1
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Multiyear climate predictions with two initialization strategies are systematically assessed in the EC-Earth V2.3 climate model. In one ensemble, an estimate of the observed climate state is used to initialize the model. The other...
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Multiyear climate predictions with two initialization strategies are systematically assessed in the EC-Earth V2.3 climate model. In one ensemble, an estimate of the observed climate state is used to initialize the model. The other uses estimates of observed ocean and sea ice anomalies on top of the model climatology. The ensembles show similar spatial characteristics of drift related to the biases in control simulations. As expected, the drift is less with anomaly initialization. The full field initialization overshoots to a colder state which is related to cold biases in the tropics and North Atlantic, associated with oceanic processes. Despite different amplitude of the drift, both ensembles show similar skill in multiyear global temperature predictions, but regionally differences are found. On multiyear time scales, initialization with observations enhances both deterministic and probabilistic skill scores in the North Atlantic. The probabilistic verification shows skill over the European continent. Key PointsMultiyear climate variability is predictableDifferent initialization strategies give similar resultsThere is scope for probabilistic predictability in Europe.
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Flash droughts have been occurring frequently worldwide, which has a serious impact on food and water security. The rapid onset of flash droughts presents a challenge to the subseasonal forecast, but there is limited knowl-edge ab...
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Flash droughts have been occurring frequently worldwide, which has a serious impact on food and water security. The rapid onset of flash droughts presents a challenge to the subseasonal forecast, but there is limited knowl-edge about their forecast skills due to the lack of appropriate identification and assessment procedures. Here, we inves-tigate the forecast skill of flash droughts over China with lead times up to 3 weeks by using hindcast datasets from the Subseasonal-to-Seasonal Prediction (S2S) project. The flash droughts are identified by using weekly soil moisture per-centiles from two S2S forecast models (ECMWF and NCEP). The comparison with reanalysis shows that ECMWF and NCEP forecast models underestimate flash drought occurrence by 5% and 19% for lead 1 week. The national mean hit rates for flash droughts are 0.22 and 0.16 for ECMWF and NCEP models for lead 1 week, and they can reach 0.29 and 0.18 over South China. The ensemble of the two models increases equitable threat score (ETS) from ECMWF and NCEP models by 8% and 40% for lead 1 week. In terms of probabilistic forecast, ECMWF has a higher Brier skill score than NCEP, especially over eastern China, which is consistent with higher temperature and precipitation forecast skill. The multimodel ensemble has the highest Brier skill score. This study suggests the importance of multimodel ensemble flash drought forecasting. SIGNIFICANCE STATEMENT: Flash droughts have raised considerable concern, but whether they can be pre-dicted at subseasonal time scales remains unclear. This study evaluates forecast skill of flash droughts over China based on ECMWF and NCEP hindcast data. Focusing on the historical flash drought events identified by the onset speed and duration, it is found that the ECMWF model outperformed the NCEP model with higher hit rates, lower false alarm ra-tios, and higher equitable threat scores, especially during the first week. However, less than 30% of the drought events can be captured in most regions by both models. An ensemble of the two models showed skill improvement against the ECMWF model for both deterministic and probabilistic forecasts.
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The initial condition effect on climate prediction skill over a 2-year hindcast time-scale has been assessed from ensemble HadCM3 climate model runs using anomaly initialization over the period 19902001, and making comparisons wit...
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The initial condition effect on climate prediction skill over a 2-year hindcast time-scale has been assessed from ensemble HadCM3 climate model runs using anomaly initialization over the period 19902001, and making comparisons with runs without initialization (equivalent to climatological conditions), and to anomaly persistence. It is shown that the assimilation improves the prediction skill in the first year globally, and in a number of limited areas out into the second year. Skill in hindcasting surface air temperature anomalies is most marked over ocean areas, and is coincident with areas of high sea surface temperature and ocean heat content skill. Skill improvement over land areas is much more limited but is still detectable in some cases. We found little difference in the skill of hindcasts using three different sets of ocean initial conditions, and we obtained the best results by combining these to form a grand ensemble hindcast set. Results are also compared with the idealized predictability studies of Collins (Clim. Dynam. 2002; 19: 671-692), which used the samemodel. The maximum lead time for which initialization gives enhanced skill over runs without initialization varies in different regions but is very similar to lead times found in the idealized studies, therefore strongly supporting the process representation in the model as well as its use for operational predictions. The limited 12-year period of the study, however, means that the regional details of model skill should probably be further assessed under a wider range of observational conditions. Copyright (C) 2011 Royal Meteorological Society and British Crown Copyright, the Met Office
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Decadal climate predictions are being increasingly used by stakeholders interested in the evolution of climate over the coming decade. However, investigating the added value of those initialized decadal predictions over other sour...
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Decadal climate predictions are being increasingly used by stakeholders interested in the evolution of climate over the coming decade. However, investigating the added value of those initialized decadal predictions over other sources of information typically used by stakeholders generally relies on forecast accuracy, while probabilistic aspects, although crucial to users, are often overlooked. In this study, the quality of the near-surface air temperature from initialized predictions has been assessed in terms of reliability, an essential characteristic of climate simulation ensembles, and compared to the reliability of noninitialized simulations performed with the same model ensembles. Here, reliability is defined as the capability to obtain a true estimate of the forecast uncertainty from the ensemble spread. We show the limited added value of initialization in terms of reliability, the initialized predictions being significantly more reliable than their noninitialized counterparts only for specific regions and the first forecast year. By analyzing reliability for different forecast system ensembles, we further highlight the fact that the combination of models seems to play a more important role than the ensemble size of each individual forecast system. This is due to sampling different model errors related to model physics, numerics, and initialization approaches involved in the multimodel, allowing for a certain level of error compensation. Finally, this study demonstrates that all forecast system ensembles are affected by systematic biases and dispersion errors that affect the reliability. This set of errors makes bias correction and calibration necessary to obtain reliable estimates of forecast probabilities that can be useful to stakeholders.
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The interdecadal change in seasonal predictability and numerical models' seasonal forecast skill in the Northern Hemisphere are examined using both observations and the seasonal hindcast from six coupled atmosphere-ocean climate m...
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The interdecadal change in seasonal predictability and numerical models' seasonal forecast skill in the Northern Hemisphere are examined using both observations and the seasonal hindcast from six coupled atmosphere-ocean climate models from the 21 period of 1960-1980 (P1) to that of 1981-2001 (P2). It is shown that the one-month lead seasonal forecast skill of the six models' multi-model ensemble is significantly increased from P1 to P2 for all four seasons. We identify four possible reasons accounting for the interdecadal change of the seasonal forecast skill. Firstly, the numerical model's ability to simulate the mean state, the time variability and the spatial structures of the sea surface temperature and precipitation over the tropical Pacific is improved in P2 compared to P1. Secondly, an examination of the potential predictability of the atmosphere, estimated by the ratio of the total variance to the variance due to the internal dynamics of the model atmosphere, reveals that the atmospheric potential predictability is significantly increased after 1980s which is mainly due to an increased influence of El Nio-Southern Oscillation signal over the North Pacific and North American regions. Thirdly, the long-term climate trends in the atmosphere are found to contribute, to some extent, to the increased seasonal forecast skill especially over the Eurasian regions. Finally, the improved ocean observations in P2 may provide better initial conditions for the coupled models' seasonal forecast.
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Reliable El Nino Southern Oscillation (ENSO) prediction at seasonal-to-interannual lead times would be critical for different stakeholders to conduct suitable management. In recent years, new methods combining climate network anal...
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Reliable El Nino Southern Oscillation (ENSO) prediction at seasonal-to-interannual lead times would be critical for different stakeholders to conduct suitable management. In recent years, new methods combining climate network analysis with El Nino prediction claim that they can predict El Nino up to 1 year in advance by overcoming the spring barrier problem (SPB). Usually this kind of method develops an index representing the relationship between different nodes in El Nino related basins, and the index crossing a certain threshold is taken as the warning of an El Nino event in the next few months. How well the prediction performs should be measured in order to estimate any improvements. However, the amount of El Nino recordings in the available data is limited, therefore it is difficult to validate whether these methods are truly predictive or their success is merely a result of chance. We propose a benchmarking method by surrogate data for a quantitative forecast validation for small data sets. We apply this method to a naive prediction of El Nino events based on the Oscillation Nino Index (ONI) time series, where we build a data-based prediction scheme using the index series itself as input. In order to assess the network-based El Nino prediction method, we reproduce two different climate network-based forecasts and apply our method to compare the prediction skill of all these. Our benchmark shows that using the ONI itself as input to the forecast does not work for moderate lead times, while at least one of the two climate network-based methods has predictive skill well above chance at lead times of about one year.
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Despite the gradually increasing emphasis on assessing the skill of dynamical seasonal climate predictions from the probabilistic perspective, there is a lack of in-depth understanding that an inherent relationship may exist betwe...
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Despite the gradually increasing emphasis on assessing the skill of dynamical seasonal climate predictions from the probabilistic perspective, there is a lack of in-depth understanding that an inherent relationship may exist between the probabilistic and deterministic seasonal forecast skills. In this study, we focus on investigating this relationship, through theoretical consideration based on an analytical approach and diagnostic analysis of the historical forecasts produced by multiple dynamical models. The probabilistic forecast skill is gauged in terms of its two different attributes: resolution and reliability, while the deterministic forecast skill is measured in terms of anomaly correlation (AC). Through the theoretical consideration under certain simplified assumptions, a nonlinear, monotonic relationship is analytically derived between the resolution and the AC. Subsequent diagnostic analysis shows that the resolution and AC skills of both the multimodel ensemble and its member single models indeed appear to be approximately monotonically and nonlinearly related, specifically when they are calculated in a zonally aggregated manner by which the impact of finite sample size is reduced. This observed relationship has a specific form that is consistent with what the theory predicts. In short, the theoretical result is well verified by the dynamical model forecasts. Diagnostic analysis also shows that no good relationship exists between the reliability and the AC, signifying the difference of reliability and resolution in nature. A specific application of the proven resolution-AC coherence is also demonstrated. The proved resolution-AC relationship can facilitate comparisons among various assessments of seasonal climate prediction skill from the deterministic or probabilistic perspective alone.
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Based on historical forecasts of four individual forecasting systems, we conducted multimodel ensembles (MME) to predict the sea surface temperature anomaly (SSTA) variability and assessed these methods from a deterministic and pr...
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Based on historical forecasts of four individual forecasting systems, we conducted multimodel ensembles (MME) to predict the sea surface temperature anomaly (SSTA) variability and assessed these methods from a deterministic and probabilistic point of view. To investigate the advantages and drawbacks of different deterministic MME methods, we used simple averaged MME with equal weighs (SCM) and the stepwise pattern projection method (SPPM). We measured the probabilistic forecast accuracy by Brier skill score (BSS) combined with its two components: reliability (B-rel) and resolution (B-res). The results indicated that SCM showed a high predictability in the tropical Pacific Ocean, with a correlation exceeding 0.8 with a 6-month lead time. In general, the SCM outperformed the SPPM in the tropics, while the SPPM tend to show some positive effect on the correction when at long lead times. Corrections occurred for the spring predictability barrier of ENSO, in particular for improvements when the correlation was low or the RMSE was large using the SCM method. These qualitative results are not susceptible to the selection of the hindcast periods, it is as a rule rather by chance of these individual systems. Performance of our probabilistic MME was better than the Climate Forecast System version2 (CFSv2) forecasts in forecasting COLD, NEUTRAL, and WARM SSTA categories for most regions, mainly due to the contribution of B-rel, indicating more adequate ensemble construction strategies of the MME system superior to the CFSv2.
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