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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
Stephen R. Sobie
and
Trevor Q. Murdock

Abstract

Information about snow water equivalent in southwestern British Columbia, Canada, is used for flood management, agriculture, fisheries, and water resource planning. This study evaluates whether a process-based, energy balance snow model supplied with high-resolution statistically downscaled temperature and precipitation data can effectively simulate snow water equivalent (SWE) in the mountainous terrain of this region. Daily values of SWE from 1951 to 2018 are simulated at 1-km resolution and evaluated using a reanalysis SWE product [Snow Data Assimilation System (SNODAS)], manual snow-survey measurements at 41 sites, and automated snow pillows at six locations in the study region. Simulated SWE matches observed interannual variability well (R 2 > 0.8 for annual maximum SWE), but peak SWE biases of 20%–40% occur at some sites in the study domain, and higher biases occur where observed SWE is very low. Modeled SWE displays lower bias relative to SNODAS reanalysis at most manual survey locations. Future projections for the study area are produced using 12 downscaled climate model simulations and are used to illustrate the impacts of climate change on SWE at 1°, 2°, and 3°C of warming. Model results are used to quantify spring SWE changes at different elevations of the Whistler mountain ski resort and the sensitivity of annual peak SWE in the Metropolitan Vancouver municipal watersheds to moderate temperature increases. The results both illustrate the potential utility of a process-based snow model and identify areas where the input meteorological variables could be improved.

Significance Statement

Using high-resolution (1 km) climate data, we evaluate and apply a snow model in the mountainous terrain of coastal, southwestern British Columbia, Canada. Modeling snow water equivalent at high-resolution enables better representation of snow conditions that can vary widely over short distances and elevations. At 1°, 2°, and 3°C of warming, future snow water equivalent levels at sites nearer the coast are more vulnerable to temperature increases than sites slightly higher in elevation and farther inland. Future efforts to improve the climate data may yield better agreement between simulated and observed snow levels in certain locations.

Open access
Connor J. Chapman
and
Andrew M. Carleton

Abstract

Recent climatic studies for the dominantly rain-fed agricultural U.S. Corn Belt (CB) suggest an influence of land-use/land-cover (LULC) spatial differences on convective development, set within the larger-scale (synoptic) atmospheric conditions of pressure, winds, and vertical motion. However, the potential role of soil moisture (SM) in the LULC association with atmospheric humidity, horizontal wind, and convective precipitation (CVP) has received more limited attention, mostly as modeling studies or empirical analyses for regions nonanalogous to the CB. Accordingly, we determine the categorical associations between SM and the near-surface atmospheric humidity q, with 850-hPa horizontal wind V 850 at four representative CB locations for the nine warm seasons of 2011–19. Recurring configurations of joint SM–qV 850 conducive to CVP are then identified and stratified into three phenologically distinct subseasons (early, middle, and late). We show that the stations show some statistical similarity in their SM–CVP relationships. Corn Belt CVP occurs preferentially with high humidity and southerly winds, sometimes composing a low-level jet (LLJ), particularly on early-season days having low SM and late-season days having high SM. Additionally, midseason CVP days having weaker V 850 (i.e., non-LLJ) tend to be associated with medium SM values and high humidity. Conversely, late-season CVP days are frequently characterized by high values of both SM and humidity. These empirical results are likely explained by the inferred sensible and latent heat fluxes varying according to SM content and LULC type. They provide a basis for future mesoscale modeling studies of Corn Belt SM and CVP interactions to test the hypothesized physical processes.

Significance Statement

The effects of soil moisture on precipitation are not well understood, as previous research has found contrasting results depending on study region and period of focus. We determine these associations for the Corn Belt, a humid lowland region that has received less attention than the drier neighboring Great Plains. Our study finds strong soil moisture–precipitation relationships in the presence of high humidity, which may be explained by mechanisms associated with the subseasonal cycle of vegetation activity. Additionally, our results suggest a generally weaker influence of soil moisture on precipitation for the Corn Belt than for the Great Plains, highlighting the importance of understanding how these relationships vary spatially. Future work should test the inferred surface–atmosphere mechanisms introduced here using mesoscale modeling.

Full access
Q. Huang
,
W. J. Jiang
, and
H. P. Hong

Abstract

Canada experiences a relatively large number of tornadoes, which can cause a significant amount of damage and fatalities. In this study, a preferred prediction model for the spatially varying tornado occurrence rate is developed for Canada. The development takes into account the most commonly used spatial stochastic models and the underreporting that is due to low population density. It incorporates the annual average cloud-to-ground lightning flash (ACGLF) density and annual average thunderstorm days (ATD) as covariates in the prediction model. The model parameters estimation is carried out by using both the maximum likelihood method and the Bayesian inference. The analysis results indicate that the negative binomial model is preferable to the zero-inflated Poisson model and the Poisson model. The results show that tornado occurrence in Canada is associated with large overdispersion. Also, the statistical analysis indicates that the prediction model for the tornado occurrence rate developed on the basis of Bayesian inference is relatively insensitive to the assumed “noninformative” prior distributions. A prediction model is suggested for the spatially varying tornado occurrence rate based on the negative binomial model with the ACGLF density and ATD as covariates.

Full access
Stephen Jewson

Abstract

Knutson et al. recently published a metastudy that gives multimodel projections for changes in various properties of tropical cyclones under climate change. They considered frequency of tropical cyclones, frequency of very intense tropical cyclones, intensity of tropical cyclones, and total rainfall rate of tropical cyclones. For each of these properties, they reported changes globally and by basin for the six major tropical cyclone basins. The changes were presented as the change that would occur with 2°C warming of global mean surface temperature. These projections are potentially of great use to the tropical cyclone risk modeling community. However, most risk models use temporal baselines, such as the period from 1950 to 2019, and the Knutson et al. results can only be applied to risk models after some steps of adjustment involving past and future global mean surface temperature values. We derive the necessary adjustments and present and discuss some of the resulting projections, for different properties, basins, RCPs, and baselines. We find that the results are sensitive to the baseline being used, which implies that users of tropical cyclone risk models need to make sure they clearly understand what baseline their model represents before they adjust the model for climate change. One part of our analysis derives estimates of the implied impact of climate change so far on TC properties, relative to a representative baseline. The computer code we use to calculate the adjustments is available online.

Open access
Catherine M. Naud
,
Juan A. Crespo
, and
Derek J. Posselt

Abstract

Surface latent and sensible heat fluxes are important for extratropical cyclone evolution and intensification. Because extratropical cyclone genesis often occurs at low latitudes, Cyclone Global Navigation Satellite System (CYGNSS) surface latent and sensible heat flux retrievals are composited to provide a mean picture of their spatial distribution in low-latitude oceanic extratropical cyclones. CYGNSS heat fluxes are not affected by heavy precipitation and offer observations of storms with frequent revisit times. Consistent with prior results obtained for cyclones in the Gulf Stream region, the fluxes are strongest in the wake of the cold fronts and are weakest to negative in the warm sector in advance of the cold fronts. As cyclone strength increases or mean precipitable water decreases, the maximum in surface heat fluxes increases while the minimum decreases. This affects the changes in fluxes during cyclone intensification: the post-cold-frontal surface heat flux maximum increases as a result of the increase in near-surface winds. During cyclone dissipation, the fluxes in this sector decrease because of the decrease in winds and in temperature and humidity contrast. The warm-sector minimum decreases throughout the entire cyclone lifetime and is mostly driven by sea–air temperature and humidity contrast changes. However, during cyclone dissipation, the surface heat fluxes increase along the cold front in a narrow band to the east, independent from changes in the cyclone characteristics. This result suggests that, during cyclone dissipation, energy transfers from the ocean to the atmosphere are linked to frontal processes in addition to synoptic-scale processes.

Full access