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William I. Gustafson Jr.
and
L. Ruby Leung

Assessing future changes in air quality using downscaled climate scenarios is a relatively new application of the dynamical downscaling technique. This article compares and evaluates two downscaled simulations for the United States made using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model with the goal of understanding how errors in the downscaled climate simulations may introduce uncertainty in air quality assessment. The two downscaled simulations were driven by boundary conditions from the NCEP–NCAR global reanalysis and a global climate simulation generated by the Goddard Institute for Space Studies global circulation model, respectively. Comparisons of the model runs are made against the boundary layer and circulation characteristics of the North American Regional Reanalysis, and also against observed precipitation. The relative dependence of different simulated quantities on regional forcing, model parameterizations, and large-scale circulation provides a framework to understand similarities and differences between model simulations. Results show significant improvements in the downscaled diurnal wind patterns, in response to the complex orography, that are important for air quality assessment. Evaluation of downscaled boundary layer depth and winds, precipitation, and large-scale circulation shows larger biases related to model physics and biases in the GCM large-scale conditions. Based on the comparisons, recommendations are made to improve the utility of downscaled scenarios for air quality assessment.

Full access
L. Ruby Leung
,
Ying-Hwa Kuo
, and
Joe Tribbia
Full access
L. Ruby Leung
,
Alan F. Hamlet
,
Dennis P. Lettenmaier
, and
Arun Kumar

Natural fluctuations in the atmosphere–ocean system related to the El Niño–Southern Oscillation (ENSO) induce climate variability over many parts of the world that is potentially predictable with lead times from seasons to decades. This study examines the potential of using a model nesting approach to provide seasonal climate and streamflow forecasts suitable for water resources management. Two ensembles of perpetual January simulations were performed with a regional climate model driven by a general circulation model (GCM), using observed climatological sea surface temperature (SST) and the mean SST of the warm ENSO years between 1950 and 1994. The climate simulations were then used to drive a macroscale hydrology model to simulate streamflow. The differences between the two ensembles of simulations are defined as the warm ENSO signals.

The simulated hydroclimate signals were compared with observations. The analyses focus on the Columbia River basin in the Pacific Northwest. Results show that the global and regional models simulated a warming over the Pacific Northwest that is quite close to the observations. The models also correctly captured the strong wet signal over California and the weak dry signal over the Pacific Northwest during warm ENSO years. The regional climate model consistently performed better than the GCM in simulating the spatial distribution of regional climate and climate signals. When the climate simulations were used to drive a macroscale hydrology model at the Columbia River basin, the simulated streamflow signal resembles that derived from hydrological simulations driven by observed climate. The streamflow simulations were considerably improved when a simple bias correction scheme was applied to the climate simulations. The coupled regional climate and macroscale hydrologic simulations demonstrate the prospect for generating and utilizing seasonal climate forecasts for managing reservoirs.

Full access
Angeline G. Pendergrass
,
Peter J. Gleckler
,
L. Ruby Leung
, and
Christian Jakob
Free access
Karthik Balaguru
,
Gregory R. Foltz
,
L. Ruby Leung
,
John Kaplan
,
Wenwei Xu
,
Nicolas Reul
, and
Bertrand Chapron

Abstract

Tropical cyclone (TC) rapid intensification (RI) is difficult to predict and poses a formidable threat to coastal populations. A warm upper ocean is well known to favor RI, but the role of ocean salinity is less clear. This study shows a strong inverse relationship between salinity and TC RI in the eastern Caribbean and western tropical Atlantic due to near-surface freshening from the Amazon–Orinoco River system. In this region, rapidly intensifying TCs induce a much stronger surface enthalpy flux compared to more weakly intensifying storms, in part due to a reduction in SST cooling caused by salinity stratification. This reduction has a noticeable positive impact on TCs undergoing RI, but the impact of salinity on more weakly intensifying storms is insignificant. These statistical results are confirmed through experiments with an ocean mixed layer model, which show that the salinity-induced reduction in SST cold wakes increases significantly as the storm’s intensification rate increases. Currently, operational statistical–dynamical RI models do not use salinity as a predictor. Through experiments with a statistical RI prediction scheme, it is found that the inclusion of surface salinity significantly improves the RI detection skill, offering promise for improved operational RI prediction. Satellite surface salinity may be valuable for this purpose, given its global coverage and availability in near–real time.

Free access
Karthik Balaguru
,
Gregory R. Foltz
,
L. Ruby Leung
,
John Kaplan
,
Wenwei Xu
,
Nicolas Reul
, and
Bertrand Chapron
Full access
Christine A. Shields
,
Jonathan J. Rutz
,
L. Ruby Leung
,
F. Martin Ralph
,
Michael Wehner
,
Travis O’Brien
, and
Roger Pierce
Full access
Naomi Goldenson
,
L. Ruby Leung
,
Linda O. Mearns
,
David W. Pierce
,
Kevin A. Reed
,
Isla R. Simpson
,
Paul Ullrich
,
Will Krantz
,
Alex Hall
,
Andrew Jones
, and
Stefan Rahimi

Abstract

Dynamical downscaling is a crucial process for providing regional climate information for broad uses, using coarser-resolution global models to drive higher-resolution regional climate simulations. The pool of global climate models (GCMs) providing the fields needed for dynamical downscaling has increased from the previous generations of the Coupled Model Intercomparison Project (CMIP). However, with limited computational resources, the need for prioritizing the GCMs for subsequent downscaling studies remains. GCM selection for dynamical downscaling should focus on evaluating processes relevant for providing boundary conditions to the regional models and be inspired by regional uses such as the response of extremes to changes in the boundary conditions. This leads to the need for metrics representing processes of relevance to diverse stakeholders and subregions of a domain. Procedures to account for metric redundancy and the statistical distinguishability of GCM rankings are required. Further, procedures for selecting realizations from ensembles of top-performing GCM simulations can be used to span the range of climate change signals in multiple ways. As a result, distinct weighting of metrics and prioritization of particular realizations may depend on user needs. We provide high-level guidelines for such region-specific evaluations and address how CMIP7 might enable dynamical downscaling of a representative sample of high-quality models across representative shared socioeconomic pathways (SSPs).

Open access
Graeme Stephens
,
Jan Polcher
,
Xubin Zeng
,
Peter van Oevelen
,
Germán Poveda
,
Michael Bosilovich
,
Myoung-Hwan Ahn
,
Gianpaolo Balsamo
,
Qingyun Duan
,
Gabriele Hegerl
,
Christian Jakob
,
Benjamin Lamptey
,
Ruby Leung
,
Maria Piles
,
Zhongbo Su
,
Paul Dirmeyer
,
Kirsten L. Findell
,
Anne Verhoef
,
Michael Ek
,
Tristan L’Ecuyer
,
Rémy Roca
,
Ali Nazemi
,
Francina Dominguez
,
Daniel Klocke
, and
Sandrine Bony

Abstract

The Global Energy and Water Cycle Exchanges (GEWEX) project was created more than 30 years ago within the framework of the World Climate Research Programme (WCRP). The aim of this initiative was to address major gaps in our understanding of Earth’s energy and water cycles given a lack of information about the basic fluxes and associated reservoirs of these cycles. GEWEX sought to acquire and set standards for climatological data on variables essential for quantifying water and energy fluxes and for closing budgets at the regional and global scales. In so doing, GEWEX activities led to a greatly improved understanding of processes and our ability to predict them. Such understanding was viewed then, as it remains today, essential for advancing weather and climate prediction from global to regional scales. GEWEX has also demonstrated over time the importance of a wider engagement of different communities and the necessity of international collaboration for making progress on understanding and on the monitoring of the changes in the energy and water cycles under ever increasing human pressures. This paper reflects on the first 30 years of evolution and progress that has occurred within GEWEX. This evolution is presented in terms of three main phases of activity. Progress toward the main goals of GEWEX is highlighted by calling out a few achievements from each phase. A vision of the path forward for the coming decade, including the goals of GEWEX for the future, are also described.

Open access
Xubin Zeng
,
Lincoln Alves
,
Marie-Amélie Boucher
,
Annalisa Cherchi
,
Charlotte DeMott
,
A.P. Dimri
,
Andrew Gettelman
,
Edward Hanna
,
Takeshi Horinouchi
,
Jin Huang
,
Chris Lennard
,
L. Ruby Leung
,
Yali Luo
,
Meloth Thamban
,
Hindumathi Palanisamy
,
Sara C. Pryor
,
Marion Saint-Lu
,
Stefan P. Sobolowski
,
Detlef Stammer
,
Jakob Steiner
,
Bjorn Stevens
,
Stefan Uhlenbrook
,
Michael Wehner
, and
Paquita Zuidema

Abstract

The future state of the global water cycle and prediction of freshwater availability for humans around the world remain among the challenges of climate research and are relevant to several United Nations Sustainable Development Goals. The Global Precipitation EXperiment (GPEX) takes on the challenge of improving the prediction of precipitation quantity, phase, timing and intensity, characteristics that are products of a complex integrated system. It will achieve this by leveraging existing World Climate Research Programme (WCRP) activities and community capabilities in satellite, surface-based, and airborne observations, modeling and experimental research, and by conducting new and focused activities. It was launched in October 2023 as a WCRP Lighthouse Activity. Here we present an overview of the GPEX Science Plan that articulates the primary science questions related to precipitation measurements, process understanding, model performance and improvements, and plans for capacity development. The central phase of GPEX is the WCRP Years of Precipitation for 2-3 years with coordinated global field campaigns focusing on different storm types (atmospheric rivers, mesoscale convective systems, monsoons, and tropical cyclones, among others) over different regions and seasons. Activities are planned over the three phases (before, during, and after the Years of Precipitation) spanning a decade. These include gridded data evaluation and development, advanced modeling, enhanced understanding of processes critical to precipitation, multi-scale prediction of precipitation events across scales, and capacity development. These activities will be further developed as part of the GPEX Implementation Plan.

Open access