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Karthik Balaguru
,
Gregory R. Foltz
,
L. Ruby Leung
,
Samson M. Hagos
, and
David R. Judi

Abstract

Sea surface temperature (SST) and tropical cyclone heat potential (TCHP) are metrics used to incorporate the ocean’s influence on hurricane intensification into the National Hurricane Center’s Statistical Hurricane Intensity Prediction Scheme (SHIPS). While both SST and TCHP serve as useful measures of the upper-ocean heat content, they do not accurately represent ocean stratification effects. Here, it is shown that replacing SST within the SHIPS framework with a dynamic temperature T dy, which accounts for the oceanic negative feedback to the hurricane’s intensity arising from storm-induced vertical mixing and sea surface cooling, improves the model performance. While the model with SST and TCHP explains about 41% of the variance in 36-h intensity changes, replacing SST with T dy increases the variance explained to nearly 44%. These results suggest that representation of the oceanic feedback, even through relatively simple formulations such as T dy, may improve the performance of statistical hurricane intensity prediction models such as SHIPS.

Full access
Huiying Ren
,
Jian Lu
,
Z. Jason Hou
,
Tse-Chun Chen
,
L. Ruby Leung
, and
Fukai Liu

Abstract

Of great relevance to climate engineering is the systematic relationship between the radiative forcing to the climate system and the response of the system, a relationship often represented by the linear response function (LRF) of the system. However, estimating the LRF often becomes an ill-posed inverse problem due to high-dimensionality and non-unique relationships between the forcing and response. Recent advances in machine learning make it possible to address the ill-posed inverse problem through regularization and sparse system fitting. Here we develop a convolutional neural network (CNN) for regularized inversion. The CNN is trained using the surface temperature responses from a set of Green’s function perturbation experiments as imagery input data together with data sample densification. The resulting CNN model can infer the forcing pattern responsible for the temperature response from out-of-sample forcing scenarios. This promising proof-of-concept suggests a possible strategy for estimating the optimal forcing to negate certain undesirable effects of climate change. The limited success of this effort underscores the challenges of solving an inverse problem for a climate system with inherent nonlinearity.

Open access
Zhangshuan Hou
,
Huiying Ren
,
Ning Sun
,
Mark S. Wigmosta
,
Ying Liu
,
L. Ruby Leung
,
Hongxiang Yan
,
Richard Skaggs
, and
Andre Coleman

Abstract

Downscaled high-resolution climate simulations were used to provide inputs to the physics-based Distributed Hydrology Soil Vegetation Model (DHSVM), which accounts for the combined effects of snowmelt and rainfall processes, to determine spatially distributed available water for runoff (AWR). After quasi-stationary time windows were identified based on model outputs extracted for two different mountainous field sites in Colorado and California, intensity–duration–frequency (IDF) curves for precipitation and AWR were generated and evaluated at each numerical grid to provide guidance on hydrological infrastructure design. Impacts of snowmelt are found to be spatially variable due to spatial heterogeneity associated with topography according to geostatistical analyses. AWR extremes have stronger spatial continuity compared to precipitation. Snowmelt impacts on AWR are more pronounced at the wet California site than at the semiarid Colorado site. The sensitivities of AWR and precipitation IDFs to increasing greenhouse gas emissions are found to be localized and spatially variable. In subregions with significant snowfall, snowmelt can result in an AWR (e.g., 6-h 100-yr events) that is 70% higher than precipitation. For comparison, future greenhouse gas emissions may increase 6-h 100-yr precipitation and AWR by up to 50% and 80%, respectively, toward the end of this century.

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Christa D. Peters-Lidard
,
Faisal Hossain
,
L. Ruby Leung
,
Nate McDowell
,
Matthew Rodell
,
Francisco J. Tapiador
,
F. Joe Turk
, and
Andrew Wood

Abstract

The focus of this chapter is progress in hydrology for the last 100 years. During this period, we have seen a marked transition from practical engineering hydrology to fundamental developments in hydrologic science, including contributions to Earth system science. The first three sections in this chapter review advances in theory, observations, and hydrologic prediction. Building on this foundation, the growth of global hydrology, land–atmosphere interactions and coupling, ecohydrology, and water management are discussed, as well as a brief summary of emerging challenges and future directions. Although the review attempts to be comprehensive, the chapter offers greater coverage on surface hydrology and hydrometeorology for readers of this American Meteorological Society (AMS) monograph.

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Samson M. Hagos
,
L. Ruby Leung
,
Oluwayemi A. Garuba
,
Charlotte Demott
,
Bryce Harrop
,
Jian Lu
, and
Min-Seop Ahn

Abstract

It is well documented that over the tropical oceans, column-integrated precipitable water (pw) and precipitation (P) have a nonlinear relationship. In this study moisture budget analysis is used to examine this P–pw relationship in a normalized precipitable water framework. It is shown that the parameters of the nonlinear relationship depend on the vertical structure of moisture convergence. Specifically, the precipitable water values at which precipitation is balanced independently by evaporation versus by moisture convergence define a critical normalized precipitable water, pwnc. This is a measure of convective inhibition that separates tropical precipitation into two regimes: a local evaporation-controlled regime with widespread drizzle and a precipitable water–controlled regime. Most of the 17 CMIP6 historical simulations examined here have higher pwnc compared to ERA5, and more frequently they operate in the drizzle regime. When compared to observations, they overestimate precipitation over the high-evaporation oceanic regions off the equator, thereby producing a “double ITCZ” feature, while underestimating precipitation over the large tropical landmasses and over the climatologically moist oceanic regions near the equator. The responses to warming under the SSP585 scenario are also examined using the normalized precipitable water framework. It is shown that the critical normalized precipitable water value at which evaporation versus moisture convergence balance precipitation decreases as a result of the competing dynamic and thermodynamic responses to warming, resulting in an increase in drizzle and total precipitation. Statistically significant historical trends corresponding to the thermodynamic and dynamic changes are detected in ERA5 and in low-intensity drizzle precipitation in the PERSIANN precipitation dataset.

Open access
Chuan-Chieh Chang
,
Sandro W. Lubis
,
Karthik Balaguru
,
L. Ruby Leung
,
Samson M. Hagos
, and
Philip J. Klotzbach

Abstract

This study investigates the combined impacts of the Madden–Julian oscillation (MJO) and extratropical anticyclonic Rossby wave breaking (AWB) on subseasonal Atlantic tropical cyclone (TC) activity and their physical connections. Our results show that during MJO phases 2–3 (enhanced Indian Ocean convection) and 6–7 (enhanced tropical Pacific convection), there are significant changes in basinwide TC activity. The MJO and AWB collaborate to suppress basinwide TC activity during phases 6–7 but not during phases 2–3. During phases 6–7, when AWB occurs, various TC metrics including hurricanes, accumulated cyclone energy, and rapid intensification probability decrease by ∼50%–80%. Simultaneously, large-scale environmental variables, like vertical wind shear, precipitable water, and sea surface temperatures become extremely unfavorable for TC formation and intensification, compared to periods characterized by suppressed AWB activity during the same MJO phases. Further investigation reveals that AWB events during phases 6–7 occur in concert with the development of a stronger anticyclone in the lower troposphere, which transports more dry, stable extratropical air equatorward, and drives enhanced tropical SST cooling. As a result, individual AWB events in phases 6–7 can disturb the development of surrounding TCs to a greater extent than their phases 2–3 counterparts. The influence of the MJO on AWB over the western subtropical Atlantic can be attributed to the modulation of the convectively forced Rossby wave source over the tropical eastern Pacific. A significant number of Rossby waves initiating from this region during phases 5–6 propagate into the subtropical North Atlantic, preceding the occurrence of AWB events in phases 6–7.

Open access
Fengfei Song
,
Zhe Feng
,
L. Ruby Leung
,
Robert A. Houze Jr
,
Jingyu Wang
,
Joseph Hardin
, and
Cameron R. Homeyer

Abstract

Mesoscale convective systems (MCSs) are frequently observed over the U.S. Great Plains during boreal spring and summer. Here, four types of synoptically favorable environments for spring MCSs and two types each of synoptically favorable and unfavorable environments for summer MCSs are identified using self-organizing maps (SOMs) with inputs from observational data. During spring, frontal systems providing a lifting mechanism and an enhanced Great Plains low-level jet (GPLLJ) providing anomalous moisture are important features identified by SOM analysis for creating favorable dynamical and thermodynamic environments for MCS development. During summer, the composite MCS environment shows small positive convective available potential energy (CAPE) and convective inhibition (CIN) anomalies, which are in stark contrast with the large positive CAPE and negative CIN anomalies in spring. This contrast suggests that summer convection may occur even with weak large-scale dynamical and thermodynamic perturbations so MCSs may be inherently less predictable in summer. The two synoptically favorable environments identified in summer have frontal characteristics and an enhanced GPLLJ, but both shift north compared to spring. The two synoptically unfavorable environments feature enhanced upper-level ridges, but differ in the strength of the GPLLJ. In both seasons, MCS precipitation amount, area, and rate are much larger in the frontal-related MCSs than in nonfrontal MCSs. A large-scale index constructed using pattern correlation between large-scale environments and the synoptically favorable SOM types is found to be skillful for estimating MCS number, precipitation rate, and area in spring, but its explanatory power decreases significantly in summer. The low predictability of summer MCSs deserves further investigation in the future.

Full access
Zhe Feng
,
Robert A. Houze Jr.
,
L. Ruby Leung
,
Fengfei Song
,
Joseph C. Hardin
,
Jingyu Wang
,
William I. Gustafson Jr.
, and
Cameron R. Homeyer

ABSTRACT

The spatiotemporal variability and three-dimensional structures of mesoscale convective systems (MCSs) east of the U.S. Rocky Mountains and their large-scale environments are characterized across all seasons using 13 years of high-resolution radar and satellite observations. Long-lived and intense MCSs account for over 50% of warm season precipitation in the Great Plains and over 40% of cold season precipitation in the southeast. The Great Plains has the strongest MCS seasonal cycle peaking in May–June, whereas in the U.S. southeast MCSs occur year-round. Distinctly different large-scale environments across the seasons have significant impacts on the structure of MCSs. Spring and fall MCSs commonly initiate under strong baroclinic forcing and favorable thermodynamic environments. MCS genesis frequently occurs in the Great Plains near sunset, although convection is not always surface based. Spring MCSs feature both large and deep convection, with a large stratiform rain area and high volume of rainfall. In contrast, summer MCSs often initiate under weak baroclinic forcing, featuring a high pressure ridge with weak low-level convergence acting on the warm, humid air associated with the low-level jet. MCS genesis concentrates east of the Rocky Mountain Front Range and near the southeast coast in the afternoon. The strongest MCS diurnal cycle amplitude extends from the foothills of the Rocky Mountains to the Great Plains. Summer MCSs have the largest and deepest convective features, the smallest stratiform rain area, and the lowest rainfall volume. Last, winter MCSs are characterized by the strongest baroclinic forcing and the largest MCS precipitation features over the southeast. Implications of the findings for climate modeling are discussed.

Open access
Yumin Moon
,
Daehyun Kim
,
Allison A. Wing
,
Suzana J. Camargo
,
Ming Zhao
,
L. Ruby Leung
,
Malcolm J. Roberts
,
Dong-Hyun Cha
, and
Jihong Moon

Abstract

This study evaluates tropical cyclone (TC) rainfall structures in the CMIP6 HighResMIP global climate model (GCM) simulations against satellite rainfall retrievals. We specifically focus on TCs within the deep tropics (25°S–25°N). Analysis of TC rain rate composites indicates that in comparison to the satellite observations at the same intensity, many HighResMIP simulations tend to overproduce rain rates around TCs, in terms of both maximum rain rate magnitude and area-averaged rain rates. In addition, as model horizontal resolution increases, the magnitude of the peak rain rate appears to increase. However, the area-averaged rain rates decrease with increasing horizontal resolution, partly due to the TC eyewall being located closer to the TC center, thus occupying a smaller area and contributing less to the area-averaged rain rates. The effect of ocean coupling is to lower the TC rain rates, bringing them closer to the satellite observations, due to reduced horizontal moisture flux convergence and surface latent heat flux beneath TCs. Examination of horizontal rain rate distributions indicates that vertical wind shear–induced rainfall asymmetries in HighResMIP-simulated TCs are qualitatively consistent with the observations. In addition, a positive relationship is observed between the area-averaged inner-core rainfall and TC intensification likelihoods across the HighResMIP simulations, as GCM simulations producing stronger TCs more frequently have the greater rainfall close to the center, in agreement with previous theoretical and GCM simulation results.

Free access
Hong-Yi Li
,
L. Ruby Leung
,
Augusto Getirana
,
Maoyi Huang
,
Huan Wu
,
Yubin Xu
,
Jiali Guo
, and
Nathalie Voisin

Abstract

Accurately simulating hydrological processes such as streamflow is important in land surface modeling because they can influence other land surface processes, such as carbon cycle dynamics, through various interaction pathways. This study aims to evaluate the global application of a recently developed Model for Scale Adaptive River Transport (MOSART) coupled with the Community Land Model, version 4 (CLM4). To support the global implementation of MOSART, a comprehensive global hydrography dataset has been derived at multiple resolutions from different sources. The simulated runoff fields are first evaluated against the composite runoff map from the Global Runoff Data Centre (GRDC). The simulated streamflow is then shown to reproduce reasonably well the observed daily and monthly streamflow at over 1600 of the world’s major river stations in terms of annual, seasonal, and daily flow statistics. The impacts of model structure complexity are evaluated, and results show that the spatial and temporal variability of river velocity simulated by MOSART is necessary for capturing streamflow seasonality and annual maximum flood. Other sources of the simulation bias include uncertainties in the atmospheric forcing, as revealed by simulations driven by four different climate datasets, and human influences, based on a classification framework that quantifies the impact levels of large dams on the streamflow worldwide.

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