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James H. Stagge
and
Kyungmin Sung

Abstract

The standardized precipitation index (SPI) measures meteorological drought relative to historical climatology by normalizing accumulated precipitation. Longer record lengths improve parameter estimates, but these longer records may include signals of anthropogenic climate change and multidecadal natural climate fluctuations. Historically, climate nonstationarity has either been ignored or incorporated into the SPI using a quasi-stationary reference period, such as the WMO 30-yr period. This study introduces and evaluates a novel nonstationary SPI model based on Bayesian splines, designed to both improve parameter estimates for stationary climates and to explicitly incorporate nonstationarity. Using synthetically generated precipitation, this study directly compares the proposed Bayesian SPI model with existing SPI approaches based on maximum likelihood estimation for stationary and nonstationary climates. The proposed model not only reproduced the performance of existing SPI models but improved upon them in several key areas: reducing parameter uncertainty and noise, simultaneously modeling the likelihood of zero and positive precipitation, and capturing nonlinear trends and seasonal shifts across all parameters. Further, the fully Bayesian approach ensures all parameters have uncertainty estimates, including zero precipitation likelihood. The study notes that the zero precipitation parameter is too sensitive and could be improved in future iterations. The study concludes with an application of the proposed Bayesian nonstationary SPI model for nine gauges across a range of hydroclimate zones in the United States. Results of this experiment show that the model is stable and reproduces nonstationary patterns identified in prior studies, while also indicating new findings, particularly for the shape and zero precipitation parameters.

Significance Statement

We typically measure how bad a drought is by comparing it with the historical record. With long-term changes in climate or other factors, however, a typical drought today may not have been typical in the recent past. The purpose of this study is to build a model that measures drought relative to a changing climate. Our results confirm that the model is accurate and captures previously noted climate change patterns—a drier western United States, a wetter eastern United States, earlier summer weather, and more extreme wet seasons. This is significant because this model can improve drought measurement and identify recent changes in drought.

Open access
Robert S. Arthur
,
Timothy W. Juliano
,
Bianca Adler
,
Raghavendra Krishnamurthy
,
Julie K. Lundquist
,
Branko Kosović
, and
Pedro A. Jiménez

Abstract

Cold-air pools (CAPs), or stable atmospheric boundary layers that form within topographic basins, are associated with poor air quality, hazardous weather, and low wind energy output. Accurate prediction of CAP dynamics presents a challenge for mesoscale forecast models in part because CAPs occur in regions of complex terrain, where traditional turbulence parameterizations may not be appropriate. This study examines the effects of the planetary boundary layer (PBL) scheme and horizontal diffusion treatment on CAP prediction in the Weather Research and Forecasting (WRF) Model. Model runs with a one-dimensional (1D) PBL scheme and Smagorinsky-like horizontal diffusion are compared with runs that use a new three-dimensional (3D) PBL scheme to calculate turbulent fluxes. Simulations are completed in a nested configuration with 3-km/750-m horizontal grid spacing over a 10-day case study in the Columbia River basin, and results are compared with observations from the Second Wind Forecast Improvement Project. Using event-averaged error metrics, potential temperature and wind speed errors are shown to decrease both with increased horizontal grid resolution and with improved treatment of horizontal diffusion over steep terrain. The 3D PBL scheme further reduces errors relative to a standard 1D PBL approach. Error reduction is accentuated during CAP erosion, when turbulent mixing plays a more dominant role in the dynamics. Last, the 3D PBL scheme is shown to reduce near-surface overestimates of turbulence kinetic energy during the CAP event. The sensitivity of turbulence predictions to the master length-scale formulation in the 3D PBL parameterization is also explored.

Significance Statement

In this article, we demonstrate how a new framework for modeling atmospheric turbulence improves cold pool predictions, using a case study from January 2017 in the Columbia River basin (U.S. Pacific Northwest). Cold pools are regions of cold, stagnant air that form within valleys or basins, and improved forecasts could help to mitigate the risks they pose to air quality, transportation, and wind energy production. For the chosen case study, our tests show a reduction in temperature and wind speed errors by up to a factor of 2–3 relative to standard model options. These results strongly motivate continued development of the framework as well as its application to other complex weather events.

Open access
Chia-Ying Lee
,
Adam H. Sobel
,
Suzana J. Camargo
,
Michael K. Tippett
, and
Qidong Yang

Abstract

This study addresses hurricane hazard to the state of New York in past, present, and future using synthetic storms generated by the Columbia Hazard model (CHAZ) and climate inputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5), in conjunction with historical observations. The projected influence of anthropogenic climate change on future hazard is quantified by the normalized differences in statistics of hurricane hazard between the recent historical period (1951–2005) and two future periods under the representative concentration pathway 8.5 warming scenario: the near future (2006–40) and the late-twenty-first century (2070–99). Changes in return periods of storms affecting the state at given intensities are computed, as are wind hazards for individual counties. Other storm characteristics examined include hurricane intensity, forward speed, heading, and rate of change of the heading. The 10th, 25th, 50th, 75th, and 90th percentiles of these characteristics mostly change by less than 3% from the historical to the near future period. In the late-twenty-first century, CHAZ projects a clear upward trend in New York hurricane intensity as a consequence of increasing potential intensity and decreasing vertical wind shear in the vicinity. CHAZ also projects a decrease in translation speed and an increasing probability of approach from the east. Changes in hurricane wind hazard, however, are epistemically uncertain because of a fundamental uncertainty in CHAZ projections of New York State hurricane frequency in which frequency either increases or decreases depending on which humidity variable is used in the environmental index that controls genesis in the model. Thus, projected changes in the wind hazards are reported separately under storylines of increasing or decreasing frequency.

Open access
Yujie Wang
,
Yang Xiang
,
Lianchun Song
, and
Xin-Zhong Liang

Abstract

Determining the contribution of urbanization to extreme high-temperature events is essential to the coordinated development of Beijing, Tianjin, and Hebei (BTH). Based on the dynamic data of land-use change in every 5 years, this study uses the coupled WRF–Building Effect Parameterization/Building Energy Model (BEP/BEM) at 1-km grid spacing to quantify the contribution of BTH urbanization to the intensity and frequency of hourly extreme high-temperature events in summer. From 1990 to 2015, extreme events over Beijing and its south increased by ∼1.5°–2°C in intensity and by 50–100 h in frequency, both of which were even higher in central Beijing and Shijiazhuang. The increases of multiyear average urbanization contribution ratios to the intensity and frequency reached 3.3% and 51.6% at the 99% confidence level (p < 0.01) from 1990 to 2015, respectively. The corresponding contributions increased 1.8 and 1.2 times more significantly in the megacities (i.e., Beijing, Tianjin, and Shijiazhuang) than small and medium-sized cities. Therefore, the rapid urbanization has substantially enhanced the extreme high-temperature events in BTH. It is necessary to limit the urbanization growth rate and implement effective adaptation and mitigation strategies to sustain BTH development.

Open access
Yu Shu
,
Jisong Sun
, and
Jin Chenlu

Abstract

The mesoscale vortex (MV) is an important rain-producing system. In this study, the reanalysis data and satellite precipitation products are used to classify MVs into three categories: mesoscale convective vortex (MCV), mesoscale stratiform vortex (MSV), and mesoscale dry vortex (MDV). Then, these three categories of midlevel MVs in China from 2007 to 2016 are investigated. A total of 21 053 MVs are obtained. Most MVs form in the northwest of parent convection, and 45% of MVs generate secondary convection. The Tibetan Plateau is the main MV source region. Steered by the westerlies, MVs mainly move eastward. MCV is active in summer, MDV in winter, and MSV in spring and autumn. MCV diurnal variations are closely related to local topography, and MDVs mainly form around midnight. Composite analyses show that MCVs form near the high-value center of convective available potential energy at the development stage of parent convection. The composite MCV forms near the low pressure trough and the thermal ridge at 500 hPa, and a low-level jet exists to the south of the MCV center. At the initiation and maturity stages of MCV, strong convergence and divergence respectively exist at low levels and 400 hPa. The vortex circulation mainly locates near 500 hPa. Above the vortex is a warm core associated with the latent heat release, and below is a cold anomaly related to the cold pool. In the downshear region, there is strong low-level convergence and ascending motion, higher humidity, and greater latent heat release, which favor the formation of secondary convection.

Open access
Douglas E. Pirhalla
,
Cameron C. Lee
,
Scott C. Sheridan
, and
Varis Ransibrahmanakul

Abstract

Anomalous sea levels along the mid-Atlantic and South Atlantic coasts of the United States are often linked to atmosphere–ocean dynamics, remote- and local-scale forcing, and other factors linked to cyclone passage, winds, waves, and storm surge. Herein, we examine sea level variability along the U.S. Atlantic coast through satellite altimeter and coastal tide gauge data within the context of synoptic-scale weather pattern forcing. Altimetry data, derived from sea level anomaly (SLA) data between 1993 and 2019, were compared with self-organizing map (SOM)-based atmospheric circulation and surface wind field categorizations to reveal spatiotemporal patterns and their interrelationships with high-water-level conditions at tide gauges. Regional elevated sea level patterns and variability were strongly associated with synergistic patterns of atmospheric circulation and wind. Recurring atmospheric patterns associated with high-tide flooding events and flood risk were identified, as were specific regional oceanographic variability patterns of SLA response. The incorporation of combined metrics of wind and circulation patterns further isolate atmospheric drivers of high-tide flood events and may have particular significance for predicting future flood events over multiple spatial and temporal scales.

Significance Statement

Mean sea level and minor to moderate coastal flood events, also called blue-sky or high-tide floods, are increasing along many U.S. coastlines. While the drivers of such events are numerous, here we identified key contributing weather patterns and environmental factors linked to increased risk of regional and local high-water conditions along the Atlantic coast. Our results indicate that the predictability of elevated sea levels and high-tide floods is highly dependent upon atmospheric drivers including wind and circulation patterns and, if applied in a tested modeling framework, may prove useful for predicting future floods at various time scales.

Open access
Noah T. Plymale
,
Joshua E. Szekely
, and
Anna H. Rubinstein

Abstract

Atmospheric aerosols originating from natural and anthropogenic sources have important implications for modeling atmospheric phenomena, but aerosol conditions can change significantly and rapidly because of their dependence on local geography and atmospheric conditions. In this work, we applied a computational k-means clustering algorithm to a global set of data obtained from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), to yield a set of 25 clusters that discriminate on the basis of land type, elevation, and atmospheric conditions to predict statistical aerosol optical depth (AOD) information. We considered different subsets of MERRA-2 data, consisting of all the data averaged over a single year (2016) as well as data averaged by meteorological season over a span of five years (2012–16), arriving at five separate sets of 25 clusters. We make the clustered AOD information available with decision trees, qualitative cluster descriptions, and color-coded cluster maps to assist in identifying which cluster to use in retrieving AOD information. The results of this analysis have applications in atmospheric modeling where knowledge of approximate or typical aerosol conditions is needed in lookup-table form without requiring access to large atmospheric databases or computationally intensive aerosol models; such applications could include quick-turnaround or large-volume analyses of atmospheric conditions required to inform decision-making that affects national security, such as in modeling remote sensing and estimating upper and lower bounds for visible and infrared photon transport.

Open access
Patrick Hawbecker
and
Jason C. Knievel

Abstract

A novel algorithm is developed for detecting and classifying the Chesapeake Bay breeze and similar water-body breezes in output from mesoscale numerical weather prediction (NWP) models. To assess the generality of the new model-based detection algorithm (MBDA), it is tested on simulations from the Weather Research and Forecasting (WRF) Model and on analyses and forecasts from the High-Resolution Rapid Refresh (HRRR) model. The MBDA outperforms three observation-based detection algorithms (OBDAs) when applied to the same model output. In addition, by defining the onshore wind directions on the basis of model land-use data and not on the actual geography of the region of interest, performance of the OBDAs with model output can be improved. Although simulations by the WRF Model were used to develop the new MBDA, it performed best when applied to HRRR analyses. The generality of the MBDA is promising, and additional tuning of its parameters might improve it further.

Open access
Ke Shi
,
Yoshiya Touge
, and
So Kazama

Abstract

Droughts are widespread disasters worldwide and are concurrently influenced by multiple large-scale climate signals. This is particularly true over Japan, where drought has strong heterogeneity due to multiple factors such as monsoon, topography, and ocean circulations. Regional heterogeneity poses challenges for drought prediction and management. To overcome this difficulty, this study provides a comprehensive analysis of teleconnection between climate signals and homogeneous drought zones over Japan. First, droughts are characterized by simulated soil moisture from a land surface model during 1958–2012. The Mclust toolkit, distinct empirical orthogonal function, and wavelet coherence analysis are used, respectively, to investigate the homogeneous drought zone, principal component of each homogeneous zone, and teleconnection between climate signals and drought. Results indicate that nine homogeneous drought zones with different characteristics are defined and quantified. Among these nine zones, zone 1 is dominated by extreme drought events. Zones 2 and 6 are typical representatives of spring droughts, whereas zone 7 is wet for most of the period. The Hokkaido region is divided into wetter zone 4 and drier zone 9. Zones 3, 5, and 8 are distinguished by the topography. The analyses also reveal almost all nine zones have a high level of homogeneity, with more than 60% explained variance. Also, these nine zones are dominated by different large-scale climate signals: the Arctic Oscillation has the strongest impact on zones 1, 7, and 8; the influence of the North Atlantic Oscillation on zones 3, 4, and 6 is significant; zones 2 and 9 are both dominated by the Pacific decadal oscillation; and El Niño–Southern Oscillation dominates zone 5. The results will be valuable for drought management and drought prevention.

Open access
Aude Lemonsu
,
Cécile de Munck
,
Emilie Redon
,
Valéry Masson
,
Pascal Keravec
,
Fabrice Rodriguez
,
Laetitia Pineau
, and
Dominique Legain

Abstract

Several urban canopy models now incorporate urban vegetation to represent local urban cooling related to natural soil and plant evapotranspiration. Nevertheless, little is known about the realism of simulating these processes and turbulent exchanges within the urban canopy. Here, the coupled modeling of thermal and hydrological exchanges was investigated for a lawn located in an urban environment and for which soil temperature and water content measurements were available. The ISBA diffusive (ISBA-DF) surface–vegetation–atmosphere transfer model is inline coupled to the Town Energy Balance urban canopy model to model mixed urban environments. For the present case study, ISBA-DF was applied to the lawn and first evaluated in its default configuration. Particular attention was then paid to the parameterization of turbulent exchanges above the lawn and to the description of soil characteristics. The results highlighted the importance of taking into account local roughness related to surrounding obstacles for computing the turbulent exchanges over the lawn and simulating realistic surface and soil temperatures. The soil nature and texture vertical heterogeneity are also key properties for simulating the soil water content evolution and water exchanges.

Open access