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Jing Lei, Zhengguo Shi, Xiaoning Xie, Yingying Sha, Xinzhou Li, Xiaodong Liu, and Zhisheng An

Abstract

The westerly jet (WJ) is an important component of atmospheric circulation, which is characterized by prominent seasonal variations in intensity and position. However, the response of the WJ over Asia during the Last Glacial Maximum (LGM) is still not clear. Using general circulation model experiments, the seasonal behaviors of the WJ over central Asia and Japan are analyzed in this paper. The results show that, compared to the present day (PD), the WJ presents a complicated response during the LGM, both in intensity and position. Over central Asia, it becomes weaker in both summer and winter. But over Japan, it is enhanced in summer but becomes diminished in winter. In terms of position, the WJ over central Asia shifts southward in both summer and winter, whereas the WJ over Japan moves southward in summer but does not change obviously relative to PD in winter. Such WJ changes are well explained by meridional temperature gradients in high troposphere, which is closely linked to seasonal thermal anomalies over the Tibetan Plateau (TP). Despite cooler LGM conditions, the anomalous warming center over the TP becomes stronger in summer. Derived from the heat budget equation, the stronger heating center is mainly caused by the weaker adiabatic cooling generated from ascending motion over the area south of the TP. In winter, the cooling over the TP is also strengthened, mostly owing to the subsidence-induced weaker adiabatic heating. Due to the importance of the WJ, the potential role of TP thermal effects should be a focus when explaining the East Asian climate change during the LGM.

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Kelsey B. Thompson, Monte G. Bateman, and John R. Mecikalski

Abstract

A total of 13 ocean-based wind events from 2018, detected by buoys and Coastal-Marine Automated Network (C-MAN) stations, were analyzed using 1-min mesoscale sector Advanced Baseline Imager (ABI) cloud top brightness temperature (CTTB) data, as well as 1-min Geostationary Lightning Mapper (GLM) lightning data. The ABI and GLM instruments are located on the Geostationary Operational Environmental Satellite-16 (GOES-16) satellite. An oceanic wind event was defined as a buoy or C-MAN station-recorded peak wind gust of at least 14 m s−1, associated with a convective storm. The wind gust was required to exceed the wind speed by at least 4 m s−1 at the time of the event, but not exceed the corresponding wind speed by at least 4 m s−1 for more than 30 min. This study hypothesized that prior to a wind event, there should be unique signatures in ABI CTTB and GLM lightning datasets. The presumption was that the minimum CTTB and maximum flash rate should occur near the same time and prior to the event. The minimum CTTB occurred an average of 10.5 min and a median of 7 min prior to events, with a range from 29 min prior to 1 min after the event. Changes in CTTB were often subtle. A maximum flash rate occurred within 5 min of the minimum CTTB for 11 of the 12 events with lightning and did not exceed 11 flashes per minute for 9 of the 12 events with lightning. Operational weather forecasters might use CTTB and lightning trends to help identify storms capable of producing significant oceanic wind events.

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Jason M. Cordeira and F. Martin Ralph

Abstract

The ability to provide accurate forecasts and improve situational awareness of atmospheric rivers (ARs) is key to impact-based decision support services and applications such as forecast-informed reservoir operations. The purpose of this study is to quantify the cool-season water year skill for 2017–20 of the NCEP Global Ensemble Forecast System forecasts of integrated water vapor transport along the U.S. West Coast commonly observed during landfalling ARs. This skill is summarized for ensemble probability-over-threshold forecasts of integrated water vapor transport magnitudes ≥ 250 kg m−1 s−1 (referred to as P 250). The P 250 forecasts near North-Coastal California at 38°N, 123°W were reliable and successful at lead times of ~8–9 days with an average success ratio > 0.5 for P 250 forecasts ≥ 50% at lead times of 8 days and Brier skill scores > 0.1 at a lead time of 8–9 days. Skill and accuracy also varied as a function of latitude and event characteristics. The highest (lowest) success ratios and probability of detection values for P 250 forecasts ≥ 50% occurred on average across Northern California and Oregon (Southern California), whereas the average probability of detection of more intense and longer duration landfalling ARs was 0.1–0.2 higher than weaker and shorter duration events at lead times of 3–9 days. The potential for these forecasts to enhance situational awareness may also be improved, depending on individual applications, by allowing for flexibility in the location and time of verification; the success ratios increased 10%–30% at lead times of 5–10 days allowing for flexibility of ±1.0° latitude and ±6 h in verification.

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Craig S. Schwartz, Glen S. Romine, and David C. Dowell

Abstract

Using the Weather Research and Forecasting Model, 80-member ensemble Kalman filter (EnKF) analyses with 3-km horizontal grid spacing were produced over the entire conterminous United States (CONUS) for 4 weeks using 1-h continuous cycling. For comparison, similarly configured EnKF analyses with 15-km horizontal grid spacing were also produced. At 0000 UTC, 15- and 3-km EnKF analyses initialized 36-h, 3-km, 10-member ensemble forecasts that were verified with a focus on precipitation. Additionally, forecasts were initialized from operational Global Ensemble Forecast System (GEFS) initial conditions (ICs) and experimental “blended” ICs produced by combining large scales from GEFS ICs with small scales from EnKF analyses using a low-pass filter. The EnKFs had stable climates with generally small biases, and precipitation forecasts initialized from 3-km EnKF analyses were more skillful and reliable than those initialized from downscaled GEFS and 15-km EnKF ICs through 12–18 and 6–12 h, respectively. Conversely, after 18 h, GEFS-initialized precipitation forecasts were better than EnKF-initialized precipitation forecasts. Blended 3-km ICs reflected the respective strengths of both GEFS and high-resolution EnKF ICs and yielded the best performance considering all times: blended 3-km ICs led to short-term forecasts with similar or better skill and reliability than those initialized from unblended 3-km EnKF analyses and ~18–36-h forecasts possessing comparable quality as GEFS-initialized forecasts. This work likely represents the first time a convection-allowing EnKF has been continuously cycled over a region as large as the entire CONUS, and results suggest blending high-resolution EnKF analyses with low-resolution global fields can potentially unify short-term and next-day convection-allowing ensemble forecast systems under a common framework.

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Barbara Scherllin-Pirscher, Andrea K. Steiner, Richard A. Anthes, M. Joan Alexander, Simon P. Alexander, Riccardo Biondi, Thomas Birner, Joowan Kim, William J. Randel, Seok-Woo Son, Toshitaka Tsuda, and Zhen Zeng

Abstract

Global positioning system (GPS) radio occultation (RO) observations, first made of Earth’s atmosphere in 1995, have contributed in new ways to the understanding of the thermal structure and variability of the tropical upper troposphere–lower stratosphere (UTLS), an important component of the climate system. The UTLS plays an essential role in the global radiative balance, the exchange of water vapor, ozone, and other chemical constituents between the troposphere and stratosphere, and the transfer of energy from the troposphere to the stratosphere. With their high accuracy, precision, vertical resolution, and global coverage, RO observations are uniquely suited for studying the UTLS and a broad range of equatorial waves, including gravity waves, Kelvin waves, Rossby and mixed Rossby–gravity waves, and thermal tides. Because RO measurements are nearly unaffected by clouds, they also resolve the upper-level thermal structure of deep convection and tropical cyclones as well as volcanic clouds. Their low biases and stability from mission to mission make RO observations powerful tools for studying climate variability and trends, including the annual cycle and intraseasonal-to-interannual atmospheric modes of variability such as the quasi-biennial oscillation (QBO), Madden–Julian oscillation (MJO), and El Niño–Southern Oscillation (ENSO). These properties also make them useful for evaluating climate models and detection of small trends in the UTLS temperature, key indicators of climate change. This paper reviews the contributions of RO observations to the understanding of the three-dimensional structure of tropical UTLS phenomena and their variability over time scales ranging from hours to decades and longer.

Open access
Kelly Helm Smith, Mark E. Burbach, Michael J. Hayes, Patrick E. Guinan, Andrew J. Tyre, Brian Fuchs, Tonya Haigh, and Mark D. Svoboda

Abstract

Drought-related decision-making and policy should go beyond numeric hydrometeorological data to incorporate information on how drought affects people, livelihoods, and ecosystems. The effects of drought are nested within environmental and human systems, and relevant data may not exist in readily accessible form. For example, drought may reduce forage growth, compounded by both late-season freezes and management decisions. An effort to gather crowdsourced drought observations in Missouri in 2018 yielded a much higher number of observations than did previous related efforts. Here we examine 1) the interests, circumstances, history, and recruitment messaging that coincided to produce a high number of reports in a short time; 2) whether and how information from volunteer observers was useful to state decision-makers and to U.S. Drought Monitor (USDM) authors; and 3) potential for complementary use of stakeholder and citizen science reports in assessing trustworthiness of volunteer-provided information. State officials and the Cattlemen’s Association made requests for reports, clearly linked to improving the accuracy of the USDM and the related financial benefit. Well-timed requests provided a focus for people’s energy and a reason to invest their time. State officials made use of the dense spatial coverage that observers provided. USDM authors were very cautious about a surge of reports coinciding closely with financial incentives linked to the Livestock Forage Disaster program. An after-the-fact comparison between stakeholder reports and parallel citizen science reports suggests that the two could be complementary, with potential for developing protocols to facilitate real-time use.

Open access
Xianwen Jing, Xianglei Huang, Xiuhong Chen, Dong L. Wu, Peter Pilewskie, Odele Coddington, and Erik Richard

Abstract

Not only total solar irradiance (TSI) but spectral solar irradiance (SSI) matter for our climate. Different surfaces can have different reflectivity for the visible (VIS) and near-infrared (NIR). The recent NASA TSIS-1 mission has provided more accurate SSI observations than before. The TSI observed by TSIS-1 differs from the counterpart used by climate models by no more than 1 W m–2. However, the SSI difference in a given VIS (e.g., 0.44–0.63 μm) and NIR (e.g., 0.78–1.24 μm) band can be as large as 4 W m–2 with opposite signs. Using the NCAR CESM2, we study to what extent such different VIS and NIR SSI partitions can affect the simulated climate. Two sets of simulations with identical TSI are carried out, one with SSI partitioning as observed by the TSIS-1 mission and the other with what has been used in the current climate models. Due to different VIS-NIR spectral reflectance contrasts between icy (or snowy) surfaces and open water, the simulation with more SSI in the VIS has less solar absorption by the high-latitude surfaces, ending up with colder polar surface temperature and larger sea ice coverage. The difference is more prominent over the Antarctic than over the Arctic. Our results suggest that, even for the identical TSI, the surface albedo feedback can be triggered by different SSI partition between the VIS and NIR. The results underscore the importance of continuously monitoring SSI and the use of correct SSI in climate simulations.

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Stephen S. Leroy, Chi O. Ao, Olga P. Verkhoglyadova, and Mayra I. Oyola

Abstract

Bayesian interpolation has previously been proposed as a strategy to construct maps of radio occultation (RO) data, but that proposition did not consider the diurnal dimension of RO data. In this work, the basis functions of Bayesian interpolation are extended into the domain of the diurnal cycle, thus enabling monthly mapping of radio occultation data in synoptic time and analysis of the atmospheric tides. The basis functions are spherical harmonics multiplied by sinusoids in the diurnal cycle up to arbitrary spherical harmonic degree and diurnal cycle harmonic. Bayesian interpolation requires a regularizer to impose smoothness on the fits it produces, thereby preventing the overfitting of data. In this work, a formulation for the regularizer is proposed and the most probable values of the parameters of the regularizer determined. Special care is required when obvious gaps in the sampling of the diurnal cycle are known to occur in order to prevent the false detection of statistically significant high-degree harmonics of the diurnal cycle in the atmosphere. Finally, this work probes the ability of Bayesian interpolation to generate a valid uncertainty analysis of the fit. The post-fit residuals of Bayesian interpolation are dominated not by measurement noise but by unresolved variability in the atmosphere, which is statistically non-uniform across the globe, thus violating the central assumption of Bayesian interpolation. The problem is ameliorated by constructing maps of RO data using Bayesian interpolation that partially resolve the temporal variability of the atmosphere, constructing maps for approximately every three days of RO data.

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Ioannis Sofokleous, Adriana Bruggeman, Silas Michaelides, Panos Hadjinicolaou, George Zittis, and Corrado Camera

Abstract

A stepwise evaluation method and a comprehensive scoring approach are proposed and implemented to select a model setup and physics parameterizations of the Weather Research and Forecasting (WRF) model for high-resolution precipitation simulations. The ERA5 reanalysis data were dynamically downscaled to 1-km resolution for the topographically complex domain of the eastern Mediterranean island of Cyprus. The performance of the simulations was examined for three domain configurations, two model initialization approaches and 18 combinations of atmospheric physics parameterizations. Two continuous and two categorical scores were used for the evaluation. A new extreme event score, which combines hits and frequency bias, was introduced as a complementary evaluator of extremes. A composite scaled score was used to identify the overall best performing parameterizations. The least errors in mean daily and monthly precipitation amounts and daily extremes were found for the domain configuration with the largest extent and three nested domains. A 5-day initialization frequency did not improve precipitation, relative to 30-day continuous simulations. The parameterization type with the largest impact on precipitation was microphysics. The cumulus parameterization was also found to have an impact on the 1-km nested domain, despite that it was only activated in the coarser “parent” domains. Comparison of simulations with 12-, 4- and 1-km resolution revealed the better skill of the model at 1km. The impact of the various model configurations in the small-sized domain was different from the impact in larger model domains; this could be further explored for other atmospheric variables.

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Courtney Quinn, Dylan Harries, and Terence J. O’Kane

Abstract

The dynamics of the North Atlantic Oscillation (NAO) are analyzed through a data-driven model obtained from atmospheric reanalysis data. We apply a regularized vector autoregressive clustering technique to identify recurrent and persistent states of atmospheric circulation patterns in the North Atlantic sector (110°W-0°E, 20°N-90°N). In order to analyze the dynamics associated with the resulting cluster-based models, we define a time-dependent linear delayed map with a switching sequence set a priori by the cluster affiliations at each time step. Using a method for computing the covariant Lyapunov vectors (CLVs) over various time windows, we produce sets of mixed singular vectors (for short windows) and approximate the asymptotic CLVs (for longer windows). The growth rates and alignment of the resulting time-dependent vectors are then analyzed. We find that the window chosen to compute the vectors acts as a filter on the dynamics. For short windows, the alignment and changes in growth rates are indicative of individual transitions between persistent states. For long windows, we observe an emergent annual signal manifest in the alignment of the CLVs characteristic of the observed seasonality in the NAO index. Analysis of the average finite-time dimension reveals the NAO as the most unstable state relative to the NAO+, with persistent AR states largely stable. Our results agree with other recent theoretical and empirical studies that have shown blocking events to have less predictability than periods of enhanced zonal flow.

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