Are Midtwentieth Century Forced Changes in North Atlantic Hurricane Potential Intensity Detectable?

The impact of anthropogenic forcings on tropical North Atlantic hurricane potential intensity (PI) is evaluated in Climate Model Intercomparison Project 5 models for the period 1958–2005. Eleven models are examined, but only seven models have a forced response that is distinguishable from internal variability. The use of discriminant analysis to optimize detectability does not yield a clear, common climate change signal. Of the seven models with a significant response, one has a negative linear trend while two have a positive linear trend. The trend in PI is not even consistent among reanalyses, although this difference is not statistically significant because of large uncertainties. Furthermore, estimates of PI internal variability have significantly different variances among different reanalysis products. These disagreements between models, reanalysis products, and between models and reanalyses, in conjunction with relatively large uncertainties, highlight the difficulty of detecting and attributing observed changes in North Atlantic hurricane potential intensity

Seasonal Noise Versus Subseasonal Signal: Forecasts of California Precipitation During the Unusual Winters of 2015–2016 and 2016–2017

Subseasonal forecasts of California precipitation during the unusual winters of 2015–2016 and 2016–2017 are examined in this study. It is shown that two different ensemble forecast systems were able to predict monthly precipitation anomalies in California during these periods with some skill in forecasts initialized near or at the start of the month. The unexpected anomalies in February 2016, as well as in January and February 2017, were associated with shifts in the position of the jet stream over the northeast Pacific in a manner broadly consistent with associations found in larger ensembles of forecasts. These results support the broader notion that what is unpredictable atmospheric noise at the seasonal time scale can become predictable signal at the subseasonal time scale, despite that the lead times and verification averaging times associated with these forecasts are outside the predictability horizons of canonical midrange weather forecasting

An Empirical Relation between U.S. Tornado Activity and Monthly Environmental Parameters

In previous work the authors demonstrated an empirical relation, in the form of an index, between U.S. monthly tornado activity and monthly averaged environmental parameters. Here a detailed comparison is made between the index and reported tornado activity. The index is a function of two environmental parameters taken from the North American Regional Reanalysis: convective precipitation (cPrcp) and storm relative helicity (SRH). Additional environmental parameters are considered for inclusion in the index, among them convective available potential energy, but their inclusion does not significantly improve the overall climatological performance of the index. The aggregate climatological dependence of reported monthly U.S. tornado numbers on cPrcp and SRH is well described by the index, although it fails to capture nonsupercell and cool season tornadoes. The contributions of the two environmental parameters to the index annual cycle and spatial distribution are examined with the seasonality of cPrcp (maximum during summer) relative to SRH (maximum in winter) accounting for the index peak value in May. The spatial distribution of SRH establishes the central U.S. “tornado alley” of the index, while the spatial distribution of cPrcp enhances index values in the South and Southeast and suppresses them west of the Rockies and over elevation. At the scale of the NOAA climate regions, the largest deficiency of the index climatology occurs over the central region where the index peak in spring is too low and where the late summer drop-off in the reported number of tornadoes is poorly captured. This index deficiency is related to its sensitivity to SRH, and increasing the index sensitivity to SRH improves the representation of the annual cycle in this region. The ability of the index to represent the interannual variability of the monthly number of U.S. tornadoes can be ascribed during most times of the year to interannual variations of cPrcp rather than of SRH. However, both factors are important during the peak spring period. The index shows some skill in representing the interannual variability of monthly tornado numbers at the scale of NOAA climate regions

Rapid intensification and the bimodal distribution of tropical cyclone intensity

The severity of a tropical cyclone (TC) is often summarized by its lifetime maximum intensity (LMI), and the climatological LMI distribution is a fundamental feature of the climate system. The distinctive bimodality of the LMI distribution means that major storms (LMI >96 kt) are not very rare compared with less intense storms. Rapid intensification (RI) is the dramatic strengthening of a TC in a short time, and is notoriously difficult to forecast or simulate. Here we show that the bimodality of the LMI distribution reflects two types of storms: those that undergo RI during their lifetime (RI storms) and those that do not (non-RI storms). The vast majority (79%) of major storms are RI storms. Few non-RI storms (6%) become major storms. While the importance of RI has been recognized in weather forecasting, our results demonstrate that RI also plays a crucial role in the TC climatology

Probabilistic Multiple Linear Regression Modeling for Tropical Cyclone Intensity

The authors describe the development and verification of a statistical model relating tropical cyclone (TC) intensity to the local large-scale environment. A multiple linear regression framework is used to estimate the expected intensity of a tropical cyclone given the environmental and storm conditions. The uncertainty of the estimate is constructed from the empirical distribution of model errors. NCEP–NCAR reanalysis fields and historical hurricane data from 1981 to 1999 are used for model development, and data from 2000 to 2012 are used to evaluate model performance. Seven predictors are selected: initial storm intensity, the change of storm intensity over the past 12 h, the storm translation speed, the difference between initial storm intensity and its corresponding potential intensity, deep-layer (850–200 hPa) vertical shear, atmospheric stability, and 200-hPa divergence. The system developed here models storm intensity changes in response to changes in the surrounding environment with skill comparable to existing operational forecast tools. Since one application of such a model is to predict changes in TC activity in response to natural or anthropogenic climate change, the authors examine the performance of the model using data that is most readily available from global climate models, that is, monthly averages. It is found that statistical models based on monthly data (as opposed to daily) with only a few essential predictors, for example, the difference between storm intensity and potential intensity, perform nearly as well at short leads as when daily predictors are used

Testing the Performance of Tropical Cyclone Genesis Indices in Future Climates Using the HiRAM Model

Tropical cyclone genesis indices (TCGIs) are functions of the large-scale environment that are designed to be proxies for the probability of tropical cyclone (TC) genesis. While the performance of TCGIs in the current climate can be assessed by direct comparison to TC observations, their ability to represent future TC activity based on projections of the large-scale environment cannot. Here the authors examine the performance of TCGIs in high-resolution atmospheric model simulations forced with sea surface temperatures (SST) of future, warmer climate scenarios. They investigate whether the TCGIs derived for the present climate can, when computed from large-scale fields taken from future climate simulations, capture the simulated global mean decreases in TC frequency. The TCGIs differ in their choice of environmental predictors, and several choices of predictors perform well in the present climate. However, some TCGIs that perform well in the present climate do not accurately reproduce the simulated future decrease in TC frequency. This decrease is captured when the humidity predictor is the column saturation deficit rather than relative humidity. Using saturation deficit with relative SST as the other thermodynamic predictor overpredicts the TC frequency decrease, while using potential intensity in place of relative SST as the other thermodynamic predictor gives a good prediction of the decrease’s magnitude. These positive results appear to depend on the spatial and seasonal patterns in the imposed SST changes; none of the indices capture correctly the frequency decrease in simulations with spatially uniform climate forcings, whether a globally uniform increase in SST of 2 K, or a doubling of CO2 with no change in SST

20+ Years of Cloud-to-Ground Lightning Observations in the U.S., and Comparison with Climatological Co-variates

No abstract availabl

A genesis index for monsoon disturbances

Synoptic-scale monsoon disturbances produce the majority of continental rainfall in the monsoon regions of South Asia and Australia, yet there is little understanding of the conditions that foster development of these low pressure systems. Here a genesis index is used to associate monsoon disturbance genesis in a global domain with monthly mean, climatological environmental variables. This monsoon disturbance genesis index (MDGI) is based on four objectively selected variables: total column water vapor, low-level absolute vorticity, an approximate measure of convective available potential energy, and midtropospheric relative humidity. A Poisson regression is used to estimate the index coefficients. Unlike existing tropical cyclone genesis indices, the MDGI is defined over both land and ocean, consistent with the fact that monsoon disturbance genesis can occur over land. The index coefficients change little from their global values when estimated separately for the Asian–Australian monsoon region or the Indian monsoon region, suggesting that the conditions favorable for monsoon disturbance genesis, and perhaps the dynamics of genesis itself, are common across multiple monsoon regions. Vertical wind shear is found to be a useful predictor in some regional subdomains; although previous studies suggested that baroclinicity may foster monsoon disturbance genesis, here genesis frequency is shown to be reduced in regions of strong climatological vertical shear. The coefficients of the MDGI suggest that monsoon disturbance genesis is fostered by humid, convectively unstable environments that are rich in vorticity. Similarities with indices used to describe the distribution of tropical cyclone genesis are discussed