Climate and weather variability in the North Atlantic region is determined largely by the North Atlantic Oscillation (NAO). The potential for skillful seasonal forecasts of the winter NAO using an ...ensemble‐based dynamical prediction system has only recently been demonstrated. Here we show that the winter predictability can be significantly improved by refining a dynamical ensemble through subsampling. We enhance prediction skill of surface temperature, precipitation, and sea level pressure over essential parts of the Northern Hemisphere by retaining only the ensemble members whose NAO state is close to a “first guess” NAO prediction based on a statistical analysis of the initial autumn state of the ocean, sea ice, land, and stratosphere. The correlation coefficient between the reforecasted and observation‐based winter NAO is significantly increased from 0.49 to 0.83 over a reforecast period from 1982 to 2016, and from 0.42 to 0.86 for a forecast period from 2001 to 2017. Our novel approach represents a successful and robust alternative to further increasing the ensemble size, and potentially can be used in operational seasonal prediction systems.
Plain Language Summary
Predicting Northern Hemisphere winter conditions, which are controlled largely by fluctuations in the pressure filed over the North Atlantic (North Atlantic Oscillation, NAO), for the next season is a major challenge. Most state‐of‐the‐art seasonal prediction systems show a correlation between observed and predicted NAOs of less than 0.30. Our novel approach uses dynamical links (teleconnections) between the autumn state of sea surface temperature in the North Atlantic, Arctic sea ice, snow in Eurasia, and stratosphere temperature over the Northern Hemisphere as predictors of the NAO in the subsequent winter to subsample a dynamical reforecast ensemble. We select only the ensemble members that consistently reproduce winter NAO states that evolve in accordance with the autumn state of these predictors. As a result the winter NAO prediction skill increases to a correlation value of 0.83. Considering these well established NAO teleconnections in our Earth system model leads to an improved prediction skill of European winter conditions, that is, surface temperature, precipitation, and sea level pressure. Our results advance seasonal prediction of European weather to a level that is usually limited to tropical regions and are relevant for a variety of societal sectors, such as global and national economies and energy and water resources.
Key Points
Seasonal NAO skill is significantly enhanced to 0.83 by combining a dynamical and statistical forecast
Enhanced NAO skill improves prediction of surface temperature, precipitation, and sea level pressure in Europe
Teleconnection‐based subsampling approach can be potentially used in operational seasonal prediction systems
Decadal predictions focus regularly on the predictability of single values, like means or extremes. In this study we investigate the prediction skill of the full underlying surface temperature ...distributions on global and European scales. We investigate initialized hindcast simulations of the Max Planck Institute Earth system model decadal prediction system and compare the distribution of seasonal daily temperatures with estimates of the climatology and uninitialized historical simulations. In the analysis we show that the initialized prediction system has advantages in particular in the North Atlantic area and allow so to make reliable predictions for the whole temperature spectrum for two to 10 years ahead. We also demonstrate that the capability of initialized climate predictions to predict the temperature distribution depends on the season.
Plain Language Summary
The usual way to make statements about temperatures two to 10 years in advance is by using one value. This could be the average, minimum or maximum temperature over some time period. Nevertheless, this simplification hides that this represents only partial information about the full distribution of temperature values. We demonstrate that a climate model is in many areas, especially over and around the North Atlantic, better in predicting the temperature multiple years ahead than assuming a constant climate. We also show that in some areas a climate model, which is starting from a specific point of observations is better than one which does not do that. This shows that it is possible and useful to apply climate predictions to predict the future not only for averages, but for the whole distribution.
Key Points
Potential of decadal prediction of temperature distributions
Variability in prediction skill vary regionally over seasons
The North Atlantic offers an important area where temperature distribution predictability is improved by initialization
Seasonal prediction systems based on Earth System Models exhibit a lower proportion of predictable signal to unpredictable noise than the actual world. This puzzling phenomena has been widely ...referred to as the signal‐to‐noise paradox (SNP). Here, we investigate the SNP in a conceptual framework of a seasonal prediction system based on the Lorenz, 1963 Model (L63). We show that the SNP is not apparent in L63, if the uncertainty assumed for the initialization of the ensemble is equal to the uncertainty in the starting conditions. However, if the uncertainty in the initialization overestimates the uncertainty in the starting conditions, the SNP is apparent. In these experiments the metric used to quantify the SNP also shows a clear lead‐time dependency on subseasonal timescales. We therefore, formulate the alternative hypothesis to previous studies that the SNP could also be related to the magnitude of the initial ensemble spread.
Plain Language Summary
Comprehensive Earth System Models seem to be better at predicting the real observed climate system than expected based on their ability to predict their own modelled climate system. This puzzling phenomena is known as the signal‐to‐noise paradox (SNP) and its origin is still under intensive scientific debate with some studies pointing to deficiencies in the model formulation. In this study we investigate under which conditions the SNP can be obtained using a simple conceptual framework for a climate prediction system based on a simple dynamical model. Our results show that the SNP can be reproduced in the absence of model deficiencies if the model overestimates the observational uncertainty. We also investigate the development of the SNP on subseasonal timescales and find a clear dependency on the lead‐time of the prediction. Our results lead us to formulate an alternative hypothesis to previous studies on the origin of the SNP.
Key Points
Whether forecasts in the Lorenz Model are reliable or not depends on the ratio of initial ensemble spread to observational uncertainty
Until predictability is lost in the Lorenz Model the level of over‐or underconfidence increases with increasing lead‐time
This study investigates initialised decadal predictions of 2-m air temperature over lead times of up to 20 years and compares them against uninitialised simulations in the time period 1960–2019. We ...demonstrate that in the North and South Atlantic, as well as in the Northeast Pacific, the effect of initialisation within the prediction persists for longer than 10 lead years. In these regions, the skill of the initialised decadal predictions does not necessarily regress back to the skill of the uninitialised simulations, which is indeed the case for other regions. We analyse the Atlantic Meridional Overturning Circulation (AMOC) and show that within the first 10 years after initialisation, it drifts towards a state, which is different from both the initial state and the state of the uninitialised simulations. We show that the AMOC stays within this new state for at least another 10 years. We find that in our decadal predictions, the correct determination of future external forcings plays an important role on the global scale, while correct initialisation increases prediction skill on the regional scale.
Ocean surface wave height in the Atlantic Ocean is strongly influenced by the North Atlantic Oscillation (NAO). Here we demonstrate for the first time a skilful seasonal forecast for wave height in ...the Atlantic Ocean, produced by a seasonal prediction system with an enhanced prediction skill of winter NAO. The improved seasonal prediction skill of the wave height reaches 0.8 in major parts of the North Atlantic. Prediction skill in the Central and South Atlantic is significantly improved due to swell propagation from better represented active wave generation regions in the North Atlantic. By subsampling, the modeling of climatological anomalies of seasonal wave height for strongly positive and negative NAO phases is considerably improved. We demonstrate the potential of an improved, subsampling‐based approach for the dynamical seasonal prediction of waves, specifically for extreme seasons during strong NAO phases, which can be implemented for operational purposes.
Plain Language Summary
Ocean surface wave height in the Atlantic Ocean depends mainly on low‐frequency atmospheric variability such as the North Atlantic Oscillation (NAO). Depending on the NAO phase, different weather regimes, mean, and extreme wind and wave conditions develop over the North Atlantic. The NAO affects the location and orientation of cyclone tracks and is therefore responsible for more frequent extreme storms during a strongly positive NAO phase. Here for the first time, we show that a state‐of‐the‐art seasonal prediction system with an enhanced prediction of winter NAO leads to better forecasting of ocean waves in the Atlantic Ocean. In major parts of the North Atlantic, the classical ensemble mean approach demonstrated a prediction skill for the seasonal mean wave height of less than 0.5 for the hindcast period from 1982 to 2017. In contrast, the ensemble subsampling approach increased the skill to up to 0.8. Modeling of the seasonal mean wave height for strongly positive and negative NAO phases is considerably improved after subsampling. Thereby, we demonstrate the potential of a subsampling approach for the prediction of wave conditions during strong NAO phases.
Key Points
Skilful dynamical seasonal prediction of ocean surface waves is improved
We demonstrate NAO as a major source of seasonal predictability of waves in the Atlantic Ocean
NAO‐based subsampling improves seasonal prediction of ocean surface waves
Abstract
Useful hindcast skill of meteorological drought, assessed with the 3-month standardized precipitation index (SPI
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), has been so far limited to one lead month (time horizon of the ...prediction). Here, we quadruple that lead time by demonstrating useful skill up to lead month 4. To obtain useful hindcast skill of meteorological drought at these long lead times, we exploit well-known El Niño-Southern Oscillation (ENSO)–precipitation teleconnections through ENSO-state conditioning. We condition initialized seasonal SPI
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hindcasts, derived from the Max-Planck-Institute Earth System Model (MPI-ESM) over the period 1982–2013, on ENSO states by exploring significant agreements between two complementary analyses: hindcast skill ENSO–composites, and observed ENSO–precipitation correlations. Such conditioned hindcast skill of meteorological drought is in MPI-ESM significant and reliable for lead months 2 to 4 in equatorial South America and southern North America during these regions’ dry ENSO phases. When a region’s dry ENSO phase is present at the initialization in autumn (ASO), predictions of meteorological drought show useful hindcast skill for the upcoming winter (DJF) in the respective region. The area of this useful hindcast skill is further enlarged in both regions when the respective region’s dry ENSO phase is already present in the antecedent summer (conditioning on ENSO states in JJA). Active ENSO events constitute windows of opportunity for drought predictions that are insufficiently covered by typical predictability analyses. For these windows, we demonstrate predictive skill at unprecedented lead times with a single model whose output is not bias corrected. This contribution exemplifies the value of ENSO-state conditioning in identifying these windows of opportunity for regions that are arguably most affected by ENSO–precipitation teleconnections. During these regions’ dry ENSO phases, reliable predictive skill of meteorological drought is at long lead times particularly valuable and moves the frontier of meteorological drought predictions.
Variability of the North Atlantic Oscillation (NAO) drives wintertime temperature anomalies in the Northern Hemisphere. Dynamical seasonal prediction systems can skilfully predict the winter NAO. ...However, prediction of the NAO‐dependent air temperature anomalies remains elusive, partially due to the low variability of predicted NAO. Here, we demonstrate a hidden potential of a multi‐model ensemble of operational seasonal prediction systems for predicting wintertime temperature by increasing the variability of predicted NAO. We identify and subsample those ensemble members which are close to NAO index statistically estimated from initial autumn conditions. In our novel multi‐model approach, the correlation prediction skill for wintertime Central Europe temperature is improved from 0.25 to 0.66, accompanied by an increased winter NAO prediction skill of 0.9. Thereby, temperature anomalies can be skilfully predicted for the upcoming winter over a large part of the Northern Hemisphere through increased variability and skill of predicted NAO.
Plain Language Summary
Wintertime temperature in the Northern Hemisphere is regulated by the variations of atmospheric pressure, represented by the so‐called North Atlantic Oscillation (NAO). The NAO's phase—negative or positive—is associated with the pathways of cold and warm air masses leading to cold or warm winters in Europe. While the NAO phase can be predicted well, predictions of the NAO‐dependent air temperature remain elusive. Specifically, it is challenging to predict the strength of the NAO, the most important requirement for the accurate prediction of wintertime temperature. Here, we improve wintertime temperature prediction by increasing the strength of the predicted NAO. We use observation based autumn Northern Hemisphere ocean and air temperature, as well as ice and snow cover for statistical estimation of the first guess NAO for the upcoming winter. Then, we sub‐select only those simulations from the multi‐model ensemble, which are consistent with our first guess NAO. As a result, based on these selected members, the wintertime temperature prediction is substantially improved over a large part of the Northern Hemisphere.
Key Points
Amplitude and skill of predicted North Atlantic Oscillation (NAO) improve significantly by subsampling of ensemble of existing seasonal prediction systems
Amplified NAO variability leads to significant improvement in predicting the upcoming winter temperature anomalies in the Northern Hemisphere
Marine heatwaves are known to have a detrimental impact on marine ecosystems, yet predicting when and where they will occur remains a challenge. Here, using a large ensemble of initialized ...predictions from an Earth System Model, we demonstrate skill in predictions of summer marine heatwaves over large marine ecosystems in the Arabian Sea seven months ahead. Retrospective forecasts of summer (June to August) marine heatwaves initialized in the preceding winter (November) outperform predictions based on observed frequencies. These predictions benefit from initialization during winters of medium to strong El Niño conditions, which have an impact on marine heatwave characteristics in the Arabian Sea. Our probabilistic predictions target spatial characteristics of marine heatwaves that are specifically useful for fisheries management, as we demonstrate using an example of Indian oil sardine (Sardinella longiceps).
Plain Language Summary
Marine heatwaves (MHWs) are prolonged extreme events associated with exceptionally high ocean water temperatures. Such events impose heat stress on marine life, and thus predicting such events is beneficial for management applications. In this work we show that the occurrence of MHWs in summer in the Arabian Sea can be skilfully predicted seven month in advance. Our prediction system benefits from the information of sea surface temperature anomalies in the eastern Pacific Ocean in the preceding winter, among other aspects. Our predictions suggest potential for using climate information in fisheries management in this region.
Key Points
Summer marine heatwaves in the Arabian Sea are predictable seven months in advance
The prediction skill in summer is mainly associated with a preceding El Niño event in winter
Probabilistic predictions of Arabian Sea area under heatwave can be tailored to benefit fisheries
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