Seasonal Prediction of Arabian Sea Marine Heatwaves

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)

Forecast-Oriented Assessment of Decadal Hindcast Skill for North Atlantic SST

We demonstrate in this paper that conventional time-averaged decadal hindcast skill estimates can overestimate or underestimate the credibility of an individual decadal climate forecast.We show that hindcast skill in a long period can be higher or lower than skill in its subperiods. Instead of using time-averaged hindcast skill measures, we propose to use the physical state of the climate system at the beginning of the forecast to judge its credibility.We analyze hindcasts of North Atlantic sea surface temperature (SST) in an initialized prediction system based on the MPI-ESM-LR for the period 1901–2010. Subpolar North Atlantic Ocean heat transport (OHT) strength at hindcast initialization largely determines the skill of these hindcasts:We find high skill after anomalously strong or weak OHT, but low skill after average OHT. This knowledge can be used to constrain conventional hindcast skill estimates to improve the assessment of credibility for a decadal forecast

The relationship between sea surface temperature anomalies, wind and translation speed and North Atlantic tropical cyclone rainfall over ocean and land

There have been increasing losses from freshwater flooding associated with United States (US) landfalling hurricanes in recent years. This study analyses the relationship between sea surface temperature anomalies (SSTA), wind and translation speed and North Atlantic tropical cyclone precipitation (TCP) for the period 1998-2017. Based on our statistical analysis of observation data, for a 1 °C SST increase in the main development region (MDR), there is a 6% increase (not statistically significant) in the TCP rate (mmhr−1) over the Atlantic, which rises to over 40% over land (US states) and appears linked not only to the Clausius-Clapeyron relationship but also to the increase in tropical cyclone (TC) intensity associated with increasing SSTA. Total annual TCP is significantly correlated with the SST in the MDR. Over the Atlantic there is an increase of 116% and over land there is an increase of 140% in total TCP for a 1 °C rise in SST in the MDR. Again, this is linked to the increase in windspeed and the number of TC tracks which also rises with positive SSTAs in the MDR. Our analysis of landfalling TC tracks for nine US states provides a systematic review and highlights how TCP varies by US state. The highest number of landfalls per year are found in Florida, North Carolina and Texas. The median tropical cyclone translation speed is 20.3kmhr−1, although this falls to 16.5 kmhr−1 over land and there is a latitudinal dependence on translation speed. Overall, we find a different TCP response to rising SST over the ocean and land, with the response over land over four times more than the Clausius-Clapeyron rate. The links between SSTA in the MDR and both TCP rate and annual total TCP provide useful insights for seasonal to decadal US flood prediction from TCs

Palaeo-sea-level and palaeo-ice-sheet databases: problems, strategies, and perspectives

Sea-level and ice-sheet databases have driven numerous advances in understanding the Earth system. We describe the challenges and offer best strategies that can be adopted to build self-consistent and standardised databases of geological and geochemical information used to archive palaeo-sea-levels and palaeo-ice-sheets. There are three phases in the development of a database: (i) measurement, (ii) interpretation, and (iii) database creation. Measurement should include the objective description of the position and age of a sample, description of associated geological features, and quantification of uncertainties Interpretation of the sample may have a subjective component, but it should always include uncertainties and alternative or contrasting interpretations, with any exclusion of existing interpretations requiring a full justification. During the creation of a database, an approach based on accessibility, transparency, trust, availability, continuity, completeness, and communication of content (ATTAC³) must be adopted. It is essential to consider the community that creates and benefits from a database. We conclude that funding agencies should not only consider the creation of original data in specific research question-oriented projects, but also include the possibility of using part of the funding for IT -related and database creation tasks, which are essential to guarantee accessibility and maintenance of the collected data

When Does the Lorenz 1963 Model Exhibit the Signal‐To‐Noise Paradox?

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‐timeCopernicus Climate Change ServiceMarine Institute and the European Regional Development fundDeutsche Forschungsgemeinschaft (DFG, German Research Foundation

Advanced information criterion for environmental data quality assurance

A new method for testing time series of environmental data for internal inconsistencies is presented. The method divides the dataset into several disjunct blocks. By means of a comparison of the blocks' estimated probability density distributions, each block is compared with the others. In order to judge the differences, four different measures are used and compared: Kullback-Leibler Divergence, Jensen-Shannon Divergence, Earth Mover's Distance and the Root Mean Square. By looking at the resulting patterns, conclusions on possible inconsistencies in the data can be drawn. This paper shows some sensitivitiy tests and gives an example for an application to real data. Furthermore, it is shown, in which cases of errors (shift in mean, shift in variance and rounding), which measure performs best

Improving seasonal predictions of meteorological drought by conditioning on ENSO states

Useful hindcast skill of meteorological drought, assessed with the 3-month standardized precipitation index (SPI $_{3M}$ ), 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 $_{3M}$ 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

Skilful Seasonal Prediction of Ocean Surface Waves in the Atlantic Ocean

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

Hidden Potential in Predicting Wintertime Temperature Anomalies in the Northern Hemisphere

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.Deutsche ForschungsgemeinschaftClimate, Climatic Change, and SocietyMarine Institute grantEuropean Union's Horizon 2020 research and innovation programmehttps://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal-original-single-levels?tab=overviewhttp://www.ecmwf.int/en/forecasts/dataset