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Sonia I. Seneviratne
,
Randal D. Koster
,
Zhichang Guo
,
Paul A. Dirmeyer
,
Eva Kowalczyk
,
David Lawrence
,
Ping Liu
,
David Mocko
,
Cheng-Hsuan Lu
,
Keith W. Oleson
, and
Diana Verseghy

Abstract

Soil moisture memory is a key aspect of land–atmosphere interaction and has major implications for seasonal forecasting. Because of a severe lack of soil moisture observations on most continents, existing analyses of global-scale soil moisture memory have relied previously on atmospheric general circulation model (AGCM) experiments, with derived conclusions that are probably model dependent. The present study is the first survey examining and contrasting global-scale (near) monthly soil moisture memory characteristics across a broad range of AGCMs. The investigated simulations, performed with eight different AGCMs, were generated as part of the Global Land–Atmosphere Coupling Experiment.

Overall, the AGCMs present relatively similar global patterns of soil moisture memory. Outliers are generally characterized by anomalous water-holding capacity or biases in radiation forcing. Water-holding capacity is highly variable among the analyzed AGCMs and is the main factor responsible for intermodel differences in soil moisture memory. Therefore, further studies on this topic should focus on the accurate characterization of this parameter for present AGCMs. Despite the range in the AGCMs’ behavior, the average soil moisture memory characteristics of the models appear realistic when compared to available in situ soil moisture observations. An analysis of the processes controlling soil moisture memory in the AGCMs demonstrates that it is mostly controlled by two effects: evaporation’s sensitivity to soil moisture, which increases with decreasing soil moisture content, and runoff’s sensitivity to soil moisture, which increases with increasing soil moisture content. Soil moisture memory is highest in regions of medium soil moisture content, where both effects are small.

Full access
Joseph A. Santanello Jr.
,
Paul A. Dirmeyer
,
Craig R. Ferguson
,
Kirsten L. Findell
,
Ahmed B. Tawfik
,
Alexis Berg
,
Michael Ek
,
Pierre Gentine
,
Benoit P. Guillod
,
Chiel van Heerwaarden
,
Joshua Roundy
, and
Volker Wulfmeyer

Abstract

Land–atmosphere (L-A) interactions are a main driver of Earth’s surface water and energy budgets; as such, they modulate near-surface climate, including clouds and precipitation, and can influence the persistence of extremes such as drought. Despite their importance, the representation of L-A interactions in weather and climate models remains poorly constrained, as they involve a complex set of processes that are difficult to observe in nature. In addition, a complete understanding of L-A processes requires interdisciplinary expertise and approaches that transcend traditional research paradigms and communities. To address these issues, the international Global Energy and Water Exchanges project (GEWEX) Global Land–Atmosphere System Study (GLASS) panel has supported “L-A coupling” as one of its core themes for well over a decade. Under this initiative, several successful land surface and global climate modeling projects have identified hot spots of L-A coupling and helped quantify the role of land surface states in weather and climate predictability. GLASS formed the Local Land–Atmosphere Coupling (LoCo) project and working group to examine L-A interactions at the process level, focusing on understanding and quantifying these processes in nature and evaluating them in models. LoCo has produced an array of L-A coupling metrics for different applications and scales and has motivated a growing number of young scientists from around the world. This article provides an overview of the LoCo effort, including metric and model applications, along with scientific and programmatic developments and challenges.

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Reinder A. Feddes
,
Holger Hoff
,
Michael Bruen
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Todd Dawson
,
Patricia de Rosnay
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Paul Dirmeyer
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Robert B. Jackson
,
Pavel Kabat
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Axel Kleidon
,
Allan Lilly
, and
Andrew J. Pitman

From 30 September to 2 October 1999 a workshop was held in Gif-sur-Yvette, France, with the central objective to develop a research strategy for the next 3–5 years, aiming at a systematic description of root functioning, rooting depth, and root distribution for modeling root water uptake from local and regional to global scales. The goal was to link more closely the weather prediction and climate and hydrological models with ecological and plant physiological information in order to improve the understanding of the impact that root functioning has on the hydrological cycle at various scales. The major outcome of the workshop was a number of recommendations, detailed at the end of this paper, on root water uptake parameterization and modeling and on collection of root and soil hydraulic data.

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Paul A. Dirmeyer
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Benjamin A. Cash
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James L. Kinter III
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Cristiana Stan
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Thomas Jung
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Lawrence Marx
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Peter Towers
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Nils Wedi
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Jennifer M. Adams
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Eric L. Altshuler
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Bohua Huang
,
Emilia K. Jin
, and
Julia Manganello

Abstract

Global simulations have been conducted with the European Centre for Medium-Range Weather Forecasts operational model run at T1279 resolution for multiple decades representing climate from the late twentieth and late twenty-first centuries. Changes in key components of the water cycle are examined, focusing on variations at short time scales. Metrics of coupling and feedbacks between soil moisture and surface fluxes and between surface fluxes and properties of the planetary boundary layer (PBL) are inspected. Features of precipitation and other water cycle trends from coupled climate model consensus projections are well simulated. Extreme 6-hourly rainfall totals become more intense over much of the globe, suggesting an increased risk for flash floods. Seasonal-scale droughts are projected to escalate over much of the subtropics and midlatitudes during summer, while tropical and winter droughts become less likely. These changes are accompanied by an increase in the responsiveness of surface evapotranspiration to soil moisture variations. Even though daytime PBL depths increase over most locations in the next century, greater latent heat fluxes also occur over most land areas, contributing a larger energy effect per unit mass of air, except over some semiarid regions. This general increase in land–atmosphere coupling is represented in a combined metric as a “land coupling index” that incorporates the terrestrial and atmospheric effects together. The enhanced feedbacks are consistent with the precipitation changes, but a causal connection cannot be made without further sensitivity studies. Nevertheless, this approach could be applied to the output of traditional climate change simulations to assess changes in land–atmosphere feedbacks.

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Paul A. Dirmeyer
,
Jiexia Wu
,
Holly E. Norton
,
Wouter A. Dorigo
,
Steven M. Quiring
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Trenton W. Ford
,
Joseph A. Santanello Jr.
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Michael G. Bosilovich
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Michael B. Ek
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Randal D. Koster
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Gianpaolo Balsamo
, and
David M. Lawrence

Abstract

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those it is found that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely because of differences in instrumentation, calibration, and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat-dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory), and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but they poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration, or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison.

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Paul A. Dirmeyer
,
Liang Chen
,
Jiexia Wu
,
Chul-Su Shin
,
Bohua Huang
,
Benjamin A. Cash
,
Michael G. Bosilovich
,
Sarith Mahanama
,
Randal D. Koster
,
Joseph A. Santanello
,
Michael B. Ek
,
Gianpaolo Balsamo
,
Emanuel Dutra
, and
David M. Lawrence

Abstract

This study compares four model systems in three configurations (LSM, LSM + GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly underrepresent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land–atmosphere coupling) and may overrepresent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally underrepresent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly, and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Although the models validate better against Bowen-ratio-corrected surface flux observations, which allow for closure of surface energy balances at flux tower sites, it is not clear whether the corrected fluxes are more representative of actual fluxes. The analysis illuminates targets for coupled land–atmosphere model development, as well as the value of long-term globally distributed observational monitoring.

Open access
Randal D. Koster
,
Y. C. Sud
,
Zhichang Guo
,
Paul A. Dirmeyer
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Gordon Bonan
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Keith W. Oleson
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Edmond Chan
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Diana Verseghy
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Peter Cox
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Harvey Davies
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Eva Kowalczyk
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C. T. Gordon
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Shinjiro Kanae
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David Lawrence
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Ping Liu
,
David Mocko
,
Cheng-Hsuan Lu
,
Ken Mitchell
,
Sergey Malyshev
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Bryant McAvaney
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Taikan Oki
,
Tomohito Yamada
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Andrew Pitman
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Christopher M. Taylor
,
Ratko Vasic
, and
Yongkang Xue

Abstract

The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.

Full access
Zhichang Guo
,
Paul A. Dirmeyer
,
Randal D. Koster
,
Y. C. Sud
,
Gordon Bonan
,
Keith W. Oleson
,
Edmond Chan
,
Diana Verseghy
,
Peter Cox
,
C. T. Gordon
,
J. L. McGregor
,
Shinjiro Kanae
,
Eva Kowalczyk
,
David Lawrence
,
Ping Liu
,
David Mocko
,
Cheng-Hsuan Lu
,
Ken Mitchell
,
Sergey Malyshev
,
Bryant McAvaney
,
Taikan Oki
,
Tomohito Yamada
,
Andrew Pitman
,
Christopher M. Taylor
,
Ratko Vasic
, and
Yongkang Xue

Abstract

The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role.

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Ned Haughton
,
Gab Abramowitz
,
Andy J. Pitman
,
Dani Or
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Martin J. Best
,
Helen R. Johnson
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Gianpaolo Balsamo
,
Aaron Boone
,
Matthias Cuntz
,
Bertrand Decharme
,
Paul A. Dirmeyer
,
Jairui Dong
,
Michael Ek
,
Zichang Guo
,
Vanessa Haverd
,
Bart J. J. van den Hurk
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Grey S. Nearing
,
Bernard Pak
,
Joe A. Santanello Jr.
,
Lauren E. Stevens
, and
Nicolas Vuichard

Abstract

The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) illustrated the value of prescribing a priori performance targets in model intercomparisons. It showed that the performance of turbulent energy flux predictions from different land surface models, at a broad range of flux tower sites using common evaluation metrics, was on average worse than relatively simple empirical models. For sensible heat fluxes, all land surface models were outperformed by a linear regression against downward shortwave radiation. For latent heat flux, all land surface models were outperformed by a regression against downward shortwave radiation, surface air temperature, and relative humidity. These results are explored here in greater detail and possible causes are investigated. It is examined whether particular metrics or sites unduly influence the collated results, whether results change according to time-scale aggregation, and whether a lack of energy conservation in flux tower data gives the empirical models an unfair advantage in the intercomparison. It is demonstrated that energy conservation in the observational data is not responsible for these results. It is also shown that the partitioning between sensible and latent heat fluxes in LSMs, rather than the calculation of available energy, is the cause of the original findings. Finally, evidence is presented that suggests that the nature of this partitioning problem is likely shared among all contributing LSMs. While a single candidate explanation for why land surface models perform poorly relative to empirical benchmarks in PLUMBER could not be found, multiple possible explanations are excluded and guidance is provided on where future research should focus.

Full 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
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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