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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.

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 sub-seasonal 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.

Useful hindcast skill of meteorological drought, assessed with the 3-month standardized precipitation index (SPI3M), 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 SPI3M 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 part

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.

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

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.