Ryan N. Maue

Comment on “Low frequency variability in globally integrated tropical cyclone power dissipation” by Ryan Sriver and Matthew Huber

[1] Sriver and Huber [2006] (hereinafter referred to as SH06), in an effort to examine low frequency tropical cyclone (TC) intensity trends, utilized atmospheric reanalysis data (ERA40 [Uppala et al., 2005] and NNR [Kalnay et al., 1996]) to develop a TC power dissipation (PD) climatology. The variance of the normalized filtered TC PD time series (SH06, Figure 1) matched up well (especially after 1978) with the results of Emanuel [2005] (hereinafter referred to as E05), who employed the best-track (BT) dataset. SH06 therefore asserted that the ERA40 TC PD climatology was an independent, uncorrected, and robust representation of trends in global TC activity. Furthermore, SH06 concluded that the power dissipation index (PDI) developed by E05 was an accurate estimate of the PD. In this comment, we question the veracity of SH06’s assertion that the ERA40 PD is an independent confirmation of E05’s findings. [2] SH06 acknowledged that the ERA40 surface wind data was perhaps unreliable prior to the assimilation of satellite observations ( 1979). Nevertheless, they claimed that the ERA40 correctly distinguished TC winds from the background wind field (SH06). Furthermore, they asserted that since the TCs were not ‘bogused’ into the ERA40, their methodology and results were ‘‘truly independent’’ of previous studies’ BT approaches (e.g., E05). [3] Upon calculating the global ERA40 PD and PDI (SH06, Figure 1), SH06 found the curves overlapped (R > 0.98) and concluded that the trends in maximum sustained winds were nearly identical to trends in area-integrated storm winds. This result is not surprising after examining the ERA40 TC wind fields. Figure 1a is the West Pacific (WPAC) and North Atlantic (NATL) basin subset of TC wind observations from 1958–2001 for the ERA40 and E05 BT. Henceforth, we address the assumptions made by SH06 concerning the ability of ERA40 to accurately and consistently depict TC winds from two viewpoints: the maximum wind speed (PDI) inside the prescribed 7 7 TC footprint and the area-integrated wind speed (PD). [4] The bottom dashed lines in Figure 1a are the ERA40 maximum wind (top line) and mean wind (bottom line) inside the storm footprint. There is no significant trend in either quantity. The overall wind speeds, either areaintegrated or maximum, are considerably less than the BT maximum sustained wind. The ERA40Mean and ERA40Max winds for major TCs also do not exhibit a significant trend throughout the dataset (Figure S1 of the auxiliary material). [5] Upon examination of our Figure 1a, we do not agree with SH06’s contention that the reliability of ERA40 surface winds prior to 1979 caused degraded correlations with the E05 BT PDI. One would expect a noticeable jump or discontinuity in the mean or maximum winds. Instead, the ERA40 consistently (albeit a considerable underestimate) depicts TC wind speeds throughout the entire 1958– 2001 period in the mean sense (WPAC + NATL). A SaffirSimpson scale breakdown is provided in Table S1 for the WPAC and the NATL combined for the preand postsatellite eras. During both eras, the difference between a Category 1 and 5 is only 1.5 m/s or about a 10% difference in wind speed with large variability. There are many instances in the ERA40 in the NATL of a Category 4+ resolved with less than 11 m/s surface winds including Hurricanes Donna (1960), Flora (1963), Edith (1971), Andrew (1992), and Cindy (1999). EPAC basin major TC representations are exceptionally weak with major TC maximum winds of 8 m/s (not shown). These results are consistent with Manning and Hart’s [2007]; SH06 erroneously described the ERA40 TC representations as ‘‘correct’’ and ‘‘robust’’. [6] In Figure 1b, the heavy black line is an unfiltered, normalized reproduction of E05 BT PDI from SH06’s Figure 1 for the WPAC + NATL. The red (green) line represents the PD (PDI) calculated from ERA40 surface winds while the blue line represents the PDI calculated using only an arbitrary constant wind value of 8 m/s. Since the energy plotted is normalized by standard deviations from the mean energy, the choice of constant wind is indeed arbitrary. [7] Figure S2 provides the global PD and correlations for the ERA40, NNR, and an arbitrary constant averaged wind speed. The correlation between the NNR and ERA40 is R = 0.96, which agrees with the nearly overlapping curves in SH06’s Figure 2. SH06 claim that this high correlation is evidence of the ‘‘robustness’’ of the reanalysis products. Again, from our previous discussion on the actual TC wind representations in the ERA40 and NNR, this claim is baseless. The area-averaged wind used in the global PD calculation, regardless of the dataset, varies little about a constant value. Hence, any arbitrary constant wind can be chosen (Figure 1b, blue line) to eliminate the year-to-year GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L11703, doi:10.1029/2006GL028283, 2007 Click Here for Full Article

The record-breaking 2015 hurricane season in the eastern North Pacific: An analysis of environmental conditions

The presence of a near-record El Nino and a positive Pacific Meridional Mode provided an extraordinarily warm background state that fueled the 2015 eastern North Pacific hurricane season to near-record levels. We find that the western portion of the eastern North Pacific, referred to as the Western Development Region (WDR; 10°–20°N, 116°W–180°), set records for named storms, hurricane days, and Accumulated Cyclone Energy in 2015. When analyzing large-scale environmental conditions, we show that record warm sea surface temperatures, high midlevel relative humidity, high low-level relative vorticity, and record low vertical wind shear were among the environmental forcing factors contributing to the observed tropical cyclone activity. We assess how intraseasonal atmospheric variability may have contributed to active and inactive periods observed during the 2015 hurricane season. We document that, historically, active seasons are associated with May–June El Nino conditions, potentially allowing for predictability of future active WDR seasons.

Recent Northern Hemisphere mid-latitude medium-range deterministic forecast skill

ABSTRACT A multi-model archive of global deterministic forecasts and analyses from three operational systems is constructed to analyse recent Northern Hemisphere mid-latitude forecast skill from 2007 to 2012 and its relation to large-scale atmospheric flow anomalies defined by the Arctic Oscillation (AO) index. We find that the anomaly correlation coefficient (ACC) in 120-hr forecasts of 500 hPa geopotential height has similar variability on synoptic, monthly, and seasonal time scales in each of the three forecast systems examined here: the European Centre for Medium-Range Weather Forecasts, the National Centers for Environmental Prediction Global Forecast System, and the U.S. Navy Operational Global Atmospheric Prediction System. The results indicate that forecast skill as measured by the ACC is significantly correlated with the AO index and its transitions between negative and positive phase. Intervals of exceptionally high ACC skill during the 2009–2010 and 2010–2011 winters are associated with periods in which the AO remained in a persistent negative phase pattern. Episodes of low ACC, including so-called ‘forecast skill dropouts’ most frequently occur during transitions between negative and positive AO index and with positive AO index. The root mean square error (RMSE) of 120-hr forecast 500 hPa height is also modulated by the AO index, but to a lesser extent than the ACC. In two recent winters, the RMSE indicates lower 120-hr forecast accuracy during periods with negative AO index, which is opposite to ‘skill’ patterns provided by the ACC. These results suggest that the ACC is not in all situations an optimal metric with which to quantify model forecast skill, since the ACC can be higher when the large-scale atmospheric flow contains strong anomalies even if there is no actual improvement in model forecasts of that atmospheric state.

A Census of Atmospheric Variability From Seconds to Decades

This paper synthesizes and summarizes atmospheric variability on time scales from seconds to decades through a phenomenological census. We focus mainly on unforced variability in the troposphere, stratosphere, and mesosphere. In addition to atmosphere-only modes, our scope also includes coupled modes, in which the atmosphere interacts with the other components of the Earth system, such as the ocean, hydrosphere, and cryosphere. The topics covered include turbulence on time scales of seconds and minutes, gravity waves on time scales of hours, weather systems on time scales of days, atmospheric blocking on time scales of weeks, the Madden–Julian Oscillation on time scales of months, the Quasi-Biennial Oscillation and El Nino–Southern Oscillation on time scales of years, and the North Atlantic, Arctic, Antarctic, Pacific Decadal, and Atlantic Multidecadal Oscillations on time scales of decades. The paper serves as an introduction to a special collection of Geophysical Research Letters on atmospheric variability. We hope that both this paper and the collection will serve as a useful resource for the atmospheric science community and will act as inspiration for setting future research directions.