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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
William J. Gutowski Jr.
,
Raymond W. Arritt
,
Sho Kawazoe
,
David M. Flory
,
Eugene S. Takle
,
Sébastien Biner
,
Daniel Caya
,
Richard G. Jones
,
René Laprise
,
L. Ruby Leung
,
Linda O. Mearns
,
Wilfran Moufouma-Okia
,
Ana M. B. Nunes
,
Yun Qian
,
John O. Roads
,
Lisa C. Sloan
, and
Mark A. Snyder

Abstract

This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10% of months during the cold half of the year (October–March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations.

Full 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
Markus Gross
,
Hui Wan
,
Philip J. Rasch
,
Peter M. Caldwell
,
David L. Williamson
,
Daniel Klocke
,
Christiane Jablonowski
,
Diana R. Thatcher
,
Nigel Wood
,
Mike Cullen
,
Bob Beare
,
Martin Willett
,
Florian Lemarié
,
Eric Blayo
,
Sylvie Malardel
,
Piet Termonia
,
Almut Gassmann
,
Peter H. Lauritzen
,
Hans Johansen
,
Colin M. Zarzycki
,
Koichi Sakaguchi
, and
Ruby Leung

Abstract

Numerical weather, climate, or Earth system models involve the coupling of components. At a broad level, these components can be classified as the resolved fluid dynamics, unresolved fluid dynamical aspects (i.e., those represented by physical parameterizations such as subgrid-scale mixing), and nonfluid dynamical aspects such as radiation and microphysical processes. Typically, each component is developed, at least initially, independently. Once development is mature, the components are coupled to deliver a model of the required complexity. The implementation of the coupling can have a significant impact on the model. As the error associated with each component decreases, the errors introduced by the coupling will eventually dominate. Hence, any improvement in one of the components is unlikely to improve the performance of the overall system. The challenges associated with combining the components to create a coherent model are here termed physics–dynamics coupling. The issue goes beyond the coupling between the parameterizations and the resolved fluid dynamics. This paper highlights recent progress and some of the current challenges. It focuses on three objectives: to illustrate the phenomenology of the coupling problem with references to examples in the literature, to show how the problem can be analyzed, and to create awareness of the issue across the disciplines and specializations. The topics addressed are different ways of advancing full models in time, approaches to understanding the role of the coupling and evaluation of approaches, coupling ocean and atmosphere models, thermodynamic compatibility between model components, and emerging issues such as those that arise as model resolutions increase and/or models use variable resolutions.

Open access
Augusto C. V. Getirana
,
Emanuel Dutra
,
Matthieu Guimberteau
,
Jonghun Kam
,
Hong-Yi Li
,
Bertrand Decharme
,
Zhengqiu Zhang
,
Agnes Ducharne
,
Aaron Boone
,
Gianpaolo Balsamo
,
Matthew Rodell
,
Ally M. Toure
,
Yongkang Xue
,
Christa D. Peters-Lidard
,
Sujay V. Kumar
,
Kristi Arsenault
,
Guillaume Drapeau
,
L. Ruby Leung
,
Josyane Ronchail
, and
Justin Sheffield

Abstract

Despite recent advances in land surface modeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-of-the-art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 1° spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to match monthly Global Precipitation Climatology Project (GPCP) and Global Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l’Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets and Gravity Recovery and Climate Experiment (GRACE) TWS estimates in two subcatchments of main tributaries (Madeira and Negro Rivers). At the basin scale, simulated ET ranges from 2.39 to 3.26 mm day−1 and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.

Full access