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S.-Y. Simon Wang, Yen-Heng Lin, Robert R. Gillies, and Kirsti Hakala

Abstract

Ongoing (2014–16) drought in the state of California has played a major role in the depletion of groundwater. Within California’s Central Valley, home to one of the world’s most productive agricultural regions, drought and increased groundwater depletion occurs almost hand in hand, but this relationship appears to have changed over the last decade. Data derived from 497 wells have revealed a continued depletion of groundwater lasting a full year after drought, a phenomenon that was not observed in earlier records before the twenty-first century. Possible causes include 1) lengthening of drought associated with amplification in the 4–6-yr drought and El Niño frequency since the late 1990s and 2) intensification of drought and increased pumping that enhances depletion. Altogether, the implication is that current groundwater storage in the Central Valley will likely continue to diminish even further in 2016, regardless of the drought status.

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D. W. Wang, H. W. Wijesekera, E. Jarosz, W. J. Teague, and W. S. Pegau

Abstract

Breaking surface waves generate layers of bubble clouds as air parcels entrain into the upper ocean through the action of turbulent motions. The turbulent diffusivity in the bubble cloud layer is investigated by combining measurements of surface winds, waves, bubble acoustic backscatter, currents, and hydrography. These measurements were made at water depths of 60–90 m on the shelf of the Gulf of Alaska near Kayak Island during late December 2012, a period when the ocean was experiencing winds and significant wave heights up to 22 m s−1 and 9 m, respectively. Vertical profiles of acoustic backscatter decayed exponentially from the wave surface with e-folding lengths of about 0.6 to 6 m, while the bubble penetration depths were about 3 to 30 m. Both e-folding lengths and bubble depths were highly correlated with surface wind and wave conditions. The turbulent diffusion coefficients, inferred from e-folding length and bubble depth, varied from about 0.01 to 0.4 m2 s−1. Analysis suggests that the turbulent diffusivity in the bubble layer can be parameterized as a function of the cube of the wind friction velocity with a proportionality coefficient that depends weakly on wave age. Furthermore, in the bubble layer, on average, the shear production of the turbulent kinetic energy estimated by the diffusion coefficients is a similar order of magnitude as the dissipation rate predicted by the wall boundary layer theory.

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Q.-S. Ge, J.-Y. Zheng, Z.-X. Hao, P.-Y. Zhang, and W.-C. Wang

Chinese historical documents that contain descriptions of weather conditions can be used for studying climate of the past hundreds or even thousands of years. In this study, the progress of reconstructing a 273-station quantitative precipitation dataset for 1736–1911—a period when records of the depth of rain infiltration (into the ground) and snow depth (above the surface) were kept in the Yu–Xue–Fen–Cun (which is part of memos routinely sent to the emperors during the Qing Dynasty) is reported. To facilitate the rainfall reconstruction, a field program of 29 sites covering different climate regimes and soil characteristics was designed for the purpose of establishing the transfer function between the rain infiltration depth and rainfall amount, while the relation between the snow depth and snowfall is obtained using instrumental measurements of recent decades. The results of the first site at Shijiazhuang (near Beijing) are reported here. The reconstruction shows that the summer and winter precipitation during 1736–1911 were generally greater than their respective 1961–90 means. Two years with extreme summer precipitation are identified—112 mm in 1792 and 1167 mm in 1801; the latter is larger than the 998 mm in 1996, which has been the most severe one of recent decades. The long-term high-resolution quantitative data can be used to study climate variability as well as to evaluate historical climate model simulations.

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Jinbo Wang, Lee-Lueng Fu, Hector S. Torres, Shuiming Chen, Bo Qiu, and Dimitris Menemenlis

Abstract

The Surface Water and Ocean Topography (SWOT) mission aims to measure the sea surface height (SSH) at a high spatial resolution using a Ka-band radar interferometer (KaRIn). The primary oceanographic objective is to characterize the ocean eddies at a spatial resolution of 15 km for 68% of the ocean surface. This resolution is derived from the ratio between the wavenumber spectrum of the conventional altimeter (projected to submesoscale) and the SWOT SSH errors. While the 15-km threshold is useful as a global approximation of the spatial scales resolved by SWOT (SWOT scale), it can be misleading for regional studies. Here we revisit the problem using a high-resolution (~2-km horizontal grid spacing) tide-resolving global ocean simulation and map the SWOT scale as a function of location and season. The results show that the SWOT scale increases, in general, from about 15 km at low latitudes to ~30–45 km at mid- and high latitudes but with a large geographical dependence. A SWOT scale smaller than 30 km is expected in the high-latitude energetic regions. The SWOT scale varies seasonally as a result of the seasonality in both the noise and ocean signals. The seasonality also has a geographical dependence. Both eddies and internal gravity waves/tides contribute significantly to the SWOT scale variation. Our analysis provides model predictions for interpreting the anticipated observations from SWOT and guidance for the development of analysis methodologies.

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Weiwei Li, Zhuo Wang, Gan Zhang, Melinda S. Peng, Stanley G. Benjamin, and Ming Zhao

Abstract

This study investigates the subseasonal variability of anticyclonic Rossby wave breaking (AWB) and its impacts on atmospheric circulations and tropical cyclones (TCs) over the North Atlantic in the warm season from 1985 to 2013. Significant anomalies in sea level pressure, tropospheric wind, and humidity fields are found over the tropical–subtropical Atlantic within 8 days of an AWB activity peak. Such anomalies may lead to suppressed TC activity on the subseasonal time scale, but a significant negative correlation between the subseasonal variability of AWB and Atlantic basinwide TC activity does not exist every year, likely due to the modulation of TCs by other factors. It is also found that AWB occurrence may be modulated by the Madden–Julian oscillation (MJO). In particular, AWB occurrence over the tropical–subtropical west Atlantic is reduced in phases 2 and 3 and enhanced in phases 6 and 7 based on the Real-Time Multivariate MJO (RMM) index. The impacts of AWB on the predictive skill of Atlantic TCs are examined using the Global Ensemble Forecasting System (GEFS) reforecasts with a forecast lead time up to 2 weeks. The hit rate of tropical cyclogenesis during active AWB episodes is lower than the long-term-mean hit rate, and the GEFS is less skillful in capturing the variations of weekly TC activity during the years of enhanced AWB activity. The lower predictability of TCs is consistent with the lower predictability of environmental variables (such as vertical wind shear, moisture, and low-level vorticity) under the extratropical influence.

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Tian-You Yu, Yadong Wang, Alan Shapiro, Mark B. Yeary, Dusan S. Zrnić, and Richard J. Doviak

Abstract

Distinct tornado spectral signatures (TSSs), which are similar to white noise spectra or have bimodal features, have been observed in both simulations and real data from Doppler radars. The shape of the tornado spectrum depends on several parameters such as the range of the tornado, wind field within the storm, and the reflectivity structure. In this work, one of the higher-order spectra (HOS), termed bispectrum, is implemented to characterize TSS, in which the Doppler spectrum is considered a 1D pattern. Bispectrum has been successfully applied to pattern recognition in other fields owing to the fact that bispectrum can retain the shape information of the signal. Another parameter, termed spectral flatness, is proposed to quantify the spectrum variations. It is shown in simulation that both parameters can characterize TSS and provide information in addition to the three spectral moments. The performance of the two parameters and the spectrum width for characterizing TSS are statistically analyzed and compared for various conditions. The potential of the three parameters for improving tornado detection is further demonstrated by tornadic time series data collected by a research Weather Surveillance Radar-1988 Doppler, KOUN, operated by the National Severe Storms Laboratory.

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Yu-Chiao Liang, Jin-Yi Yu, Eric S. Saltzman, and Fan Wang

Abstract

During 2013–15, prolonged near-surface warming in the northeastern Pacific was observed and has been referred to as the Pacific warm blob. Here, statistical analyses are conducted to show that the generation of the Pacific blob is closely related to the tropical Northern Hemisphere (TNH) pattern in the atmosphere. When the TNH pattern stays in its positive phase for extended periods of time, it generates prolonged blob events primarily through anomalies in surface heat fluxes and secondarily through anomalies in wind-induced ocean advection. Five prolonged (≥24 months) blob events are identified during the past six decades (1948–2015), and the TNH–blob relationship can be recognized in all of them. Although the Pacific decadal oscillation and El Niño can also induce an arc-shaped warming pattern near the Pacific blob region, they are not responsible for the generation of Pacific blob events. The essential feature of Pacific blob generation is the TNH-forced Gulf of Alaska warming pattern. This study also finds that the atmospheric circulation anomalies associated with the TNH pattern in the North Atlantic can induce SST variability akin to the so-called Atlantic cold blob, also through anomalies in surface heat fluxes and wind-induced ocean advection. As a result, the TNH pattern serves as an atmospheric conducting pattern that connects some of the Pacific warm blob and Atlantic cold blob events. This conducting mechanism has not previously been explored.

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Minyang Wang, Shang-Ping Xie, Samuel S. P. Shen, and Yan Du

Abstract

Mesoscale activities over the equatorial Pacific Ocean are dominated by the Rossby and Yanai modes of tropical instability waves (TIWs). The TIW-induced surface velocity has not been accurately estimated in previous diagnostic models, especially for the meridional component across the equator. This study develops a diagnostic model that retains the acceleration terms to estimate the TIW surface velocity from the satellite-observed sea surface height. Validated against moored observations, the velocity across the equator is accurately estimated for the first time, much improved from existing products. The results identify the Rossby- and Yanai-mode TIWs as the northwest–southeastward (NW–SE) velocity oscillations north of the equator and the northeast–southwestward (NE–SW) velocity oscillations on the equator, respectively. Barotropic instability is the dominant energy source of the two TIW modes. The NE–SW velocity oscillation of the Yanai mode is associated with the counterclockwise shear of the South Equatorial Current on the equator. The two TIW modes induce different sea surface temperature patterns and vertical motions. Accurate estimates of TIW velocity are important for studying equatorial ocean dynamics and climate variability in the tropical Pacific Ocean.

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Steven D. Miller, Fang Wang, Ann B. Burgess, S. McKenzie Skiles, Matthew Rogers, and Thomas H. Painter

Abstract

Runoff from mountain snowpack is an important freshwater supply for many parts of the world. The deposition of aeolian dust on snow decreases snow albedo and increases the absorption of solar irradiance. This absorption accelerates melting, impacting the regional hydrological cycle in terms of timing and magnitude of runoff. The Moderate Resolution Imaging Spectroradiometer (MODIS) Dust Radiative Forcing in Snow (MODDRFS) satellite product allows estimation of the instantaneous (at time of satellite overpass) surface radiative forcing caused by dust. While such snapshots are useful, energy balance modeling requires temporally resolved radiative forcing to represent energy fluxes to the snowpack, as modulated primarily by varying cloud cover. Here, the instantaneous MODDRFS estimate is used as a tie point to calculate temporally resolved surface radiative forcing. Dust radiative forcing scenarios were considered for 1) clear-sky conditions and 2) all-sky conditions using satellite-based cloud observations. Comparisons against in situ stations in the Rocky Mountains show that accounting for the temporally resolved all-sky solar irradiance via satellite retrievals yields a more representative time series of dust radiative effects compared to the clear-sky assumption. The modeled impact of dust on enhanced snowmelt was found to be significant, accounting for nearly 50% of the total melt at the more contaminated station sites. The algorithm is applicable to regional basins worldwide, bearing relevance to both climate process research and the operational management of water resources.

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Xuguang Wang, Hristo G. Chipilski, Craig H. Bishop, Elizabeth Satterfield, Nancy Baker, and Jeffrey S. Whitaker

Abstract

A new multiscale, ensemble-based data assimilation (DA) method, multiscale local gain form ensemble transform Kalman filter (MLGETKF), is introduced. MLGETKF allows simultaneous update of multiple scales for both the ensemble mean and perturbations through assimilating all observations at once. MLGETKF performs DA in independent local volumes, which lends the algorithm a high degree of computational scalability. The multiscale analysis is enabled through the rapid creation of many pseudoensemble perturbations via a multiscale ensemble modulation procedure. The Kalman gain that is used to update the raw background ensemble mean and perturbations is based on this modulated ensemble, which intrinsically includes multiscale model space localization. Experiments with a noncycled statistical model show that the full background covariance estimated by MLGETKF more accurately resembles the shape of the true covariance than a scale-unaware localization. The mean analysis from the best-performing MLGETKF is statistically significantly more accurate than the best-performing scale-unaware LGETKF. The accuracy of the MLGETKF analysis is more sensitive to small-scale band localization radius than large-scale band. MLGETKF is further examined in a cycling DA context with a surface quasigeostrophic model. The root-mean-square potential temperature analysis error of the best-performing MLGETKF is 17.2% lower than that of the best-performing LGETKF. MLGETKF reduces analysis errors measured in kinetic energy spectra space by 30%–80% relative to LGETKF with the largest improvement at large scales. MLGETKF deterministic and ensemble mean forecasts are more accurate than LGETKF for full and large scales up to 5–6-day lead time and for small scales up to 3–4-day lead time, gaining ~12 h–1 day of predictability.

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