Mark A. Saunders

Large contribution of sea surface warming to recent increase in Atlantic hurricane activity.

Atlantic hurricane activity has increased significantly since 1995 (refs 1–4), but the underlying causes of this increase remain uncertain. It is widely thought that rising Atlantic sea surface temperatures have had a role in this, but the magnitude of this contribution is not known. Here we quantify this contribution for storms that formed in the tropical North Atlantic, Caribbean Sea and Gulf of Mexico; these regions together account for most of the hurricanes that make landfall in the United States. We show that a statistical model based on two environmental variables—local sea surface temperature and an atmospheric wind field—can replicate a large proportion of the variance in tropical Atlantic hurricane frequency and activity between 1965 and 2005. We then remove the influence of the atmospheric wind field to assess the contribution of sea surface temperature. Our results indicate that the sensitivity of tropical Atlantic hurricane activity to August–September sea surface temperature over the period we consider is such that a 0.5 °C increase in sea surface temperature is associated with a ∼40% increase in hurricane frequency and activity. The results also indicate that local sea surface warming was responsible for ∼40% of the increase in hurricane activity relative to the 1950–2000 average between 1996 and 2005. Our analysis does not identify whether warming induced by greenhouse gases contributed to the increase in hurricane activity, but the ability of climate models to reproduce the observed relationship between hurricanes and sea surface temperature will serve as a useful means of assessing whether they are likely to provide reliable projections of future changes in Atlantic hurricane activity.

Statistical evidence links exceptional 1995 Atlantic Hurricane season to record sea warming

Tropical cyclones rank above earthquakes as the major geophysical cause of loss of life and property (Bryant, 1991; Houghton, 1994). In the United States alone, the damage bill from mainland landfalling hurricanes over the last 50 years averages

Seasonal prediction of hurricane activity reaching the coast of the United States

2.0 billion per year (Hebert et al., 1996). Years with high numbers of hurricanes provide new insight on the environmental factors influencing interannual variability; hence the interest in the exceptional 1995 Atlantic season which saw 11 hurricanes and a total of 19 tropical storms, double the 50-year average. While most environmental factors in 1995 were favourable for tropical cyclone development, we show that a factor not fully explored before, the sea surface temperature (SST) was the most significant. For the 10 degrees-20 degrees N, 20 degrees-60 degrees W region where 93% of the anomalous 1995 hurricanes developed, similar to 45 year statistical regressions show that SST is the dominating influence, independent of all known other factors, behind the interannual variance in Atlantic hurricance numbers. With this SST experiencing record warm levels in 1995, 0.66 degrees C above the 1946-1995 mean, these regressions indicate that sea warming explains 61+/-34% of the anomalous hurricane activity in 1995 to 95% confidence.

Summer snow extent heralding of the winter North Atlantic Oscillation

Much of the property damage from natural hazards in the United States is caused by landfalling hurricanes—strong tropical cyclones that reach the coast. For the southeastern Atlantic coast of the US, a statistical method for forecasting the occurrence of landfalling hurricanes for the season ahead has been reported, but the physical mechanisms linking the predictor variables to the frequency of hurricanes remain unclear. Here we present a statistical model that uses July wind anomalies between 1950 and 2003 to predict with significant and useful skill the wind energy of US landfalling hurricanes for the following main hurricane season (August to October). We have identified six regions over North America and over the east Pacific and North Atlantic oceans where July wind anomalies, averaged between heights of 925 and 400 mbar, exhibit a stationary and significant link to the energy of landfalling hurricanes during the subsequent hurricane season. The wind anomalies in these regions are indicative of atmospheric circulation patterns that either favour or hinder evolving hurricanes from reaching US shores.

Seasonal predictability of the winter NAO from north Atlantic sea surface temperatures

[1] Winter climate over the North Atlantic and European sector is modulated by the North Atlantic Oscillation (NAO). We find that the summer extent of snow cover over northern North America and northern Eurasia is linked significantly (p < 0.01) to the upcoming winter NAO state. Summers with high/low snow extent precede winters of low/high NAO index phase. We suggest the linkage arises from the summer snow-associated formation of anomalous longitudinal differences in surface air temperature with the subpolar North Atlantic. Our findings indicate the seasonal predictability of North Atlantic winter climate may be higher and extend to longer leads than thought previously.

Summer U.K. Temperature and Its Links to Preceding Eurasian Snow Cover, North Atlantic SSTs, and the NAO

[1] We examine the seasonal predictability of the winter (December-January-February) North Atlantic Oscillation (NAO) from lagged north Atlantic sea surface temperatures (SSTs) for the period 1950/1-2000/1. We identify two lagged modes of SST variability whose principal components (PCs) are correlated significantly to upcoming winter NAO indices. We use linear regression with the PCs as predictors to assess the predictability of the winter NAO from cross-validation over the full period and from replicated real-time forecasts over the recent 15 year period 1986/7-2000/1. The model anticipates, in early November, the upcoming winter NAO - for a range of NAO indices - with a correlation between 0.47 and 0.63 for 1950/ 1-2000/1, and between 0.51 and 0.65 for the replicated real-time forecast period. The model also anticipates the correct NAO sign in 67% to 75% of the last 51 winters and in 80% to 93% of the last 15 winters.

North Atlantic Oscillation impact on tropical North Atlantic winter atmospheric variability

Motivated by an attempt to predict summer (June‐August) U.K. temperatures, the time-lagged correlations between summer U.K. and European temperatures and prior snow cover, North Atlantic sea surface temperatures (SSTs), and the North Atlantic Oscillation (NAO) are examined. The analysis centers on the 30-yr period 1972‐ 2001 corresponding to the interval of reliable satellite-derived land snow cover data. A significant association is found between late winter Eurasian snow cover and upcoming summer temperatures over the British Isles and adjacent areas, this link being strongest with January‐March snow cover. Significant links are also observed between summer temperatures and the preceding late winter NAO index and with a leading principal component of North Atlantic SST variability. The physical mechanisms underlying these time-lagged correlations are investigated by studying the associated variability in large-scale atmospheric circulation over the Euro‐Atlantic sector. Seasonal expansion in the Azores high pressure system may play an important role in the time-lagged relationships. The potential seasonal predictability of summer U.K. temperatures during the period 1972‐2001 is assessed by cross-validated hindcasts and usable predictive skill is found. However, the presence and cause of temporal instability in the time-lagged relationships over longer periods of time requires further investigation.

Forecasting US insured hurricane losses

The dominant mode of windspeed variability in the wintertime tropical north Atlantic (TNA) is represented by the North Atlantic Oscillation (NAO). For the December-January-February (DJF) season the leading principal component of TNA windspeed (representing 46% of the total variance) exhibits a 0.68 correlation with the NAO time-series. We show that the NAO impact on TNA trade winds peaks in January and is statistically significant at the 99% level for each month from November through to April. This association arises through the meridional pressure gradient equatorward of the Azores high pressure covarying with the NAG. We also show that the winter NAO index determines monthly precipitation levels across the northern Caribbean throughout the following year. We suggest this rainfall impact is due to long lasting, DJF forced perturbations to the north Atlantic sea surface temperature tripole characteristic of the NAO signal.

Global validation of the along-track scanning radiometer against drifting buoys

Coastal hurricanes generate huge financial losses within the insurance industry. The relative infrequency of severe coastal hurricanes implies that empirical probability estimates of the next big loss will be unreliable. Hurricane climatologists have recently developed statistical models to forecast the level of coastal hurricane activity based on climate conditions prior to the season. Motivated by the usefulness of such models, in this chapter we analyze and model a catalog of normalized insured losses caused by hurricanes affecting the United States. The catalog of losses dates back through the twentieth century. The purpose of this work is to develop a preseason forecast tool that can be used for insurance applications. Although wind speed is directly related to damage potential, the amount of damage depends on both storm intensity and storm size. As anticipated, we found that climate conditions prior to a hurricane season provide information about possible future insured hurricane losses. The models exploit this information to predict the distribution of likely annual losses and the distributionofaworst-casecatastrophiclossaggregatedovertheentireUScoast.

Seasonal predictability of wintertime storminess over the North Atlantic

The along-track scanning radiometer (ATSR) was launched on the European Space Agencys first remote sensing satellite, ERS 1, on July 17, 1991. ATSR is designed to retrieve sea surface temperature (SST) to an accuracy of 0.25 K rms, which represents more than a factor of 2 improvement over any previously flown satellite radiometer. Early validation studies from limited regions suggest that ATSR is capable of measuring SST to near this design accuracy. We report a global validation study against quality-controlled drifting buoys by examining 280 matchups worldwide with ATSR measurements at their full (1 km) resolution. We investigate optimizing the precision of ATSR using four different SST algorithms derived using a theoretical atmospheric transmission model, combined with various techniques to reduce remnant noise and other errors. We find that a “low-noise” retrieval algorithm incorporating only the 3.7 and 11 μm nadir view channels gives the optimum precision, a global pixel precision of 0.26 K (or 0.25 K if 1/2° spatial averages are used). A standard deviation of 0.25 K against global drifting buoy data approaches the geophysical limit set by the inherent variability of the skin effect and by the buoy bulk temperature accuracy. Further progress will require comparison against quality in situ radiometer-derived skin temperatures, although the problem of obtaining sufficiently large and diverse data sets will need to be addressed.