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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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The change in predictability of monthly mean temperature in a future climate is quantified based on the Community Climate System Model, version 4. According to this model, the North Atlantic overtakes the El Nino-Southern Oscillat...
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The change in predictability of monthly mean temperature in a future climate is quantified based on the Community Climate System Model, version 4. According to this model, the North Atlantic overtakes the El Nino-Southern Oscillation (ENSO) as the dominant area of seasonal predictability by 2095. This change arises partly because ENSO becomes less variable and partly because the ENSO teleconnection pattern expands into the Atlantic. Over land, the largest change in temperature predictability occurs in the tropics and is predominantly due to a decrease in ENSO variability. The southern peninsula of Africa and northeast South America are predicted to experience significant drying in a future climate, which decreases the effective heat capacity and memory, and hence increases variance independently of ENSO changes. Extratropical land areas experience enhanced precipitation in a future climate, which decreases temperature variance by the same mechanism. Finally, the model predicts that surface temperatures near the poles will become more predictable and less variable in a future climate, primarily because melting sea ice exposes the underlying sea surface temperature, which is more predictable owing to its longer time scale. Some of these results, especially the change in ENSO variance, are known to be model dependent. This paper also advances the use of information theory to quantify predictability, including (1) deriving a quantitative relation between predictability of the first and second kinds; (2) showing how differences in predictability can be decomposed in two dramatically different ways, facilitating physical interpretation; and (3) proposing a sample estimate of mutual information whose significance can be tested using standard techniques.
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The change in predictability of monthly mean temperature in a future climate is quantified based on the Community Climate System Model, version 4. According to this model, the North Atlantic overtakes the El Nino-Southern Oscillat...
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The change in predictability of monthly mean temperature in a future climate is quantified based on the Community Climate System Model, version 4. According to this model, the North Atlantic overtakes the El Nino-Southern Oscillation (ENSO) as the dominant area of seasonal predictability by 2095. This change arises partly because ENSO becomes less variable and partly because the ENSO teleconnection pattern expands into the Atlantic. Over land, the largest change in temperature predictability occurs in the tropics and is predominantly due to a decrease in ENSO variability. The southern peninsula of Africa and northeast South America are predicted to experience significant drying in a future climate, which decreases the effective heat capacity and memory, and hence increases variance independently of ENSO changes. Extratropical land areas experience enhanced precipitation in a future climate, which decreases temperature variance by the same mechanism. Finally, the model predicts that surface temperatures near the poles will become more predictable and less variable in a future climate, primarily because melting sea ice exposes the underlying sea surface temperature, which is more predictable owing to its longer time scale. Some of these results, especially the change in ENSO variance, are known to be model dependent. This paper also advances the use of information theory to quantify predictability, including (1) deriving a quantitative relation between predictability of the first and second kinds; (2) showing how differences in predictability can be decomposed in two dramatically different ways, facilitating physical interpretation; and (3) proposing a sample estimate of mutual information whose significance can be tested using standard techniques.
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In this study, the potential predictability of the Northern America (NA) surface air temperature (SAT) was explored using an information-based predictability framework and two multiple model ensemble products: a one-tier predictio...
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In this study, the potential predictability of the Northern America (NA) surface air temperature (SAT) was explored using an information-based predictability framework and two multiple model ensemble products: a one-tier prediction by coupled models (T1), and a two-tier prediction by atmospheric models only (T2). Furthermore, the potential predictability was optimally decomposed into different modes for both T1 and T2, by extracting the most predictable structures. Emphasis was placed on the comparison of the predictability between T1 and T2. It was found that the potential predictability of the NA SAT is seasonal and spatially dependent in both T1 and T2. Higher predictability occurs in spring and winter and over the southeastern US and northwestern Canada. There is no significant difference of potential predictability between T1 and T2 for most areas of NA, although T1 has higher potential predictability than T2 in the southeastern US. Both T1 and T2 display similar most predictable components (PrCs) for the NA SAT, characterized by the inter-annual variability mod and the long-term trend mode. The first one is inherent to the tropical Pacific sea surface temperature forcing, such as the El Nino-Southern Oscillation, whereas the second one is closely associated with global warming. In general, the PrC modes can better characterize the predictability in T1 than in T2, in particular for the inter-annual variability mode in the fall. The prediction skill against observations is better measured by the PrC analysis than by principal component analysis for all seasons, indicating the stronger capability of PrCA in extracting prediction targets.
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East Asian summer monsoon (EASM) prediction is difficult because of the summer monsoon's weak and unstable linkage with El Ni?o-Southern Oscillation (ENSO) interdecadal variability and its complicated association with high-latitud...
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East Asian summer monsoon (EASM) prediction is difficult because of the summer monsoon's weak and unstable linkage with El Ni?o-Southern Oscillation (ENSO) interdecadal variability and its complicated association with high-latitude processes. Two statistical prediction schemes were developed to include the interannual increment approach to improve the seasonal prediction of the EASM's strength. The schemes were applied to three models [i.e., the Centre National de Recherches Météorologiques (CNRM), the Met Office (UKMO), and the European Centre for Medium-Range Weather Forecasts (ECMWF)] and the Multimodel Ensemble (MME) from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) results for 1961-2001. The inability of the three dynamical models to reproduce the weakened East Asian monsoon at the end of the 1970s leads to low prediction ability for the interannual variability of the EASM. Therefore, the interannual increment prediction approach was applied to overcome this issue. Scheme I contained the EASM in the form of year-to-year increments as a predictor that is derived from the direct outputs of the models. Scheme II contained two predictors: both the EASM and also the western North Pacific circulation in the form of year-to-year increments. Both the crossvalidation test and the independent hindcast experiments showed that the two prediction schemes have a much better prediction ability for the EASM than does the original scheme. This study provides an efficient approach for predicting the EASM.
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Abstract We develop a data assimilation scheme with the Icosahedral Non-hydrostatic Earth System Model (ICON-ESM) for operational decadal and seasonal climate predictions at the German weather service. For this purpose, we impleme...
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Abstract We develop a data assimilation scheme with the Icosahedral Non-hydrostatic Earth System Model (ICON-ESM) for operational decadal and seasonal climate predictions at the German weather service. For this purpose, we implement an Ensemble Kalman Filter to the ocean component as a first step towards a weakly coupled data assimilation. We performed an assimilation experiment over the period 1960–2014. This ocean-only assimilation experiment serves to initialize 10-year long retrospective predictions (hindcasts) started each year on 1 November. On multi-annual time scales, we find predictability of sea surface temperature and salinity as well as oceanic heat and salt contents especially in the North Atlantic. The mean Atlantic Meridional Overturning Circulation is realistic and the variability is stable during the assimilation. On seasonal time scales, we find high predictive skill in the tropics with highest values in variables related to the El Niño/Southern Oscillation phenomenon. In the Arctic, the hindcasts correctly represent the decreasing sea ice trend in winter and, to a lesser degree, also in summer, although sea ice concentration is generally much too low in both hemispheres in summer. However, compared to other prediction systems, prediction skill is relatively low in regions apart from the tropical Pacific due to the missing atmospheric assimilation. Further improvements of the simulated mean state of ICON-ESM, e.g. through fine-tuning of the sea ice and the oceanic circulation in the Southern Ocean, are expected to improve the predictive skill. In general, we demonstrate that our data assimilation method is successfully initializing the oceanic component of the climate system.
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A variance decomposition approach to estimate the potentially predictable component of seasonal means has been further developed to separately diagnose the boundary-forced component and the slowly varying internal dynamics compone...
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A variance decomposition approach to estimate the potentially predictable component of seasonal means has been further developed to separately diagnose the boundary-forced component and the slowly varying internal dynamics component. This decomposition of the potentially predictable component has been applied to an ensemble of ten 50-year simulations of the 500 nPa geopotential height field from the Center for Ocean-Land-Atmosphere Studies global atmospheric model forced by sea-surface temperatures.The model performance is analysed for the winter season and compared with the National Centers for Environmental Prediction reanalysis. The majority of the hemispheric potentially predictable patterns, identified as the empirical orthogonal functions ofthe potentially predictable component of the seasonal mean fields, are captured by the model. These include all the patterns forced by the El Nino-Southern Oscillation in the Southern Hemisphere, and the dominant potentially predictable patterns in the Northern Hemisphere, i.e. the Pacific-North American pattern. However, the boreal winter Western Pacific Oscillation and Tropical Northern Hemisphere pattern, which are well simulated by some other general circulation models, are not well captured.
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This study examines the role of the air-sea coupled process in the seasonal predictability of Asia-Pacific summer monsoon rainfall by comparing seasonal predictions from two carefully designed model experiments: tier 1 (fully coup...
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This study examines the role of the air-sea coupled process in the seasonal predictability of Asia-Pacific summer monsoon rainfall by comparing seasonal predictions from two carefully designed model experiments: tier 1 (fully coupled model) and tier 2 (AGCM with prescribed SSTs). In these experiments, an identical AGCM is used in both tier 1 and tier 2 predictions; the daily mean SSTs from tier 1 coupled predictions are prescribed as a boundary condition in tier 2 predictions. Both predictions start in April from 1982 to 2009, with four ensemble members for each case. The model used is the Climate Forecast System, version 2 (CFSv2), the current operational climate prediction model for seasonal-to-interannual prediction at the National Centers for Environmental Prediction (NCEP). Comparisons indicate that tier 2 predictions produce not only higher rainfall biases but also unrealistically high rainfall variations in the tropical western North Pacific (TWNP) and some coastal regions as well. While the prediction skill in terms of anomaly correlations does not present a significant difference between the two types of predictions, the root-mean-square errors (RMSEs) are clearly larger over the above-mentioned regions in the tier 2 prediction. The reduced RMSE skills in the tier 2 predictions are due to the lack of a coupling process in AGCM-alone simulations, which, particularly, results in an unrealistic SST-rainfall relationship over the TWNP region. It is suggested that for a prediction of summer monsoon rainfall over the Asia-Pacific region, it is necessary to use a coupled atmosphere-ocean (tier 1) prediction system.
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This study verifies the impact of improved ocean initial conditions on the Arctic Oscillation (AO) forecast skill by assessing the one-month lead predictability of boreal winter AO using the Pusan National University (PNU) coupled...
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This study verifies the impact of improved ocean initial conditions on the Arctic Oscillation (AO) forecast skill by assessing the one-month lead predictability of boreal winter AO using the Pusan National University (PNU) coupled general circulation model (CGCM). Hindcast experiments were performed on two versions of the model, one does not use assimilated ocean initial data (V1.0) and one does (V1.1), and the results were comparatively analyzed. The forecast skill of V1.1 was superior to that of V1.0 in terms of the correlation coefficient between the predicted and observed AO indices. In the regression analysis, V1.1 showed more realistic spatial similarities than V1.0 did in predicted sea surface temperature and atmospheric circulation fields. The authors suggest the relative importance of the contribution of the ocean initial condition to the AO forecast skill was because the ocean data assimilation increased the predictability of the AO, to some extent, through the improved interaction between tropical forcing induced by realistic sea surface temperature (SST) and atmospheric circulation. In V1.1, as in the observation, the cold equatorial Pacific SST anomalies generated the weakened tropical convection and Hadley circulation over the Pacific, resulting in a decelerated subtropical jet and accelerated polar front jet in the extratropics. The intensified polar front jet implies a stronger stratospheric polar vortex relevant to the positive AO phase; hence, surface manifestations of the reflected positive AO phase were then induced through the downward propagation of the stratospheric polar vortex. The results suggest that properly assimilated initial ocean conditions might contribute to improve the predictability of global oscillations, such as the AO, through large-scale tropical ocean-atmosphere interaction.
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Seasonal prediction of the boreal spring rains in the Greater Horn of Africa has been notoriously challenging. Predictability is markedly lower than during the autumnal rainy season. Part I of this article explored predictability ...
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Seasonal prediction of the boreal spring rains in the Greater Horn of Africa has been notoriously challenging. Predictability is markedly lower than during the autumnal rainy season. Part I of this article explored predictability at the seasonal scale, using multiple linear regression. However, the three months of the boreal spring season are clearly different climatologically and with respect to the prevailing atmospheric circulation and controls on interannual variability. For that reason, the current study follows up on Part I by examining the predictability of the three months individually. The current study utilizes 1- and 2-month lead times and the results are evaluated via cross validation. This approach provided improved skill for April and May in the equatorial rainfall region, but not for March in this region and not for the region with predominantly summer rainfall. Overall, the best predictors are shown to be atmospheric variables, most often zonal and meridional wind. Sea surface temperatures and sea level pressure provided little predictive skill.
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