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Paul A. Dirmeyer
,
Sanjiv Kumar
,
Michael J. Fennessy
,
Eric L. Altshuler
,
Timothy DelSole
,
Zhichang Guo
,
Benjamin A. Cash
, and
David Straus

Abstract

The climate system model of the National Center for Atmospheric Research is used to examine the predictability arising from the land surface initialization of seasonal climate ensemble forecasts in current, preindustrial, and projected future settings. Predictability is defined in terms of the model's ability to predict its own interannual variability. Predictability from the land surface in this model is relatively weak compared to estimates from other climate models but has much of the same spatial and temporal structure found in previous studies. Several factors appear to contribute to the weakness, including a low correlation between surface fluxes and subsurface soil moisture, less soil moisture memory (lagged autocorrelation) than other models or observations, and relative insensitivity of the atmospheric boundary layer to surface flux variations. Furthermore, subseasonal cyclical behavior in plant phenology for tropical grasses introduces spurious unrealistic predictability at low latitudes during dry seasons. Despite these shortcomings, intriguing changes in predictability are found. Areas of historical land use change appear to have experienced changes in predictability, particularly where agriculture expanded dramatically into the Great Plains of North America, increasing land-driven predictability there. In a warming future climate, land–atmosphere coupling strength generally increases, but added predictability does not always follow; many other factors modulate land-driven predictability.

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Randal D. Koster
,
Paul A. Dirmeyer
,
Andrea N. Hahmann
,
Ruben Ijpelaar
,
Lori Tyahla
,
Peter Cox
, and
Max J. Suarez

Abstract

The strength of the coupling between the land and the atmosphere, which controls, for example, the degree to which precipitation-induced soil moisture anomalies affect the overlying atmosphere and thereby the subsequent generation of precipitation, has been examined and quantified with many atmospheric general circulation models (AGCMs). Generally missing from such studies, however, is an indication of the extent to which the simulated coupling strength is model dependent. Four modeling groups have recently performed a highly controlled numerical experiment that allows an objective intermodel comparison of land–atmosphere coupling strength, focusing on short (weekly down to subhourly) timescales. The experiment essentially consists of an ensemble of 1-month simulations in which each member simulation artificially maintains the same (model specific) time series of surface prognostic variables. Differences in atmospheric behavior between the ensemble members then indicate the degree to which the state of the land surface controls atmospheric processes in that model. A comparison of the four sets of experimental results shows that coupling strength does indeed vary significantly among the AGCMs.

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Bohua Huang
,
Chul-Su Shin
,
J. Shukla
,
Lawrence Marx
,
Magdalena A. Balmaseda
,
Subhadeep Halder
,
Paul Dirmeyer
, and
James L. Kinter III

Abstract

A set of ensemble seasonal reforecasts for 1958–2014 is conducted using the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2. In comparison with other current reforecasts, this dataset extends the seasonal reforecasts to the 1960s–70s. Direct comparison of the predictability of the ENSO events occurring during the 1960s–70s with the more widely studied ENSO events since then demonstrates the seasonal forecast system’s capability in different phases of multidecadal variability and degrees of global climate change. A major concern for a long reforecast is whether the seasonal reforecasts before 1979 provide useful skill when observations, particularly of the ocean, were sparser. This study demonstrates that, although the reforecasts have lower skill in predicting SST anomalies in the North Pacific and North Atlantic before 1979, the prediction skill of the onset and development of ENSO events in 1958–78 is comparable to that for 1979–2014. In particular, the ENSO predictions initialized in April during 1958–78 show higher skill in the summer. However, the skill of the earlier predictions declines faster in the ENSO decaying phase, because the reforecasts initialized after boreal summer persistently predict lingering wind and SST anomalies over the eastern equatorial Pacific during such events. Reforecasts initialized in boreal fall overestimate the peak SST anomalies of strong El Niño events since the 1980s. Both phenomena imply that the model’s air–sea feedback is overly active in the eastern Pacific before ENSO event termination. Whether these differences are due to changes in the observing system or are associated with flow-dependent predictability remains an open question.

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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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Yongjiu Dai
,
Xubin Zeng
,
Robert E. Dickinson
,
Ian Baker
,
Gordon B. Bonan
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Michael G. Bosilovich
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A. Scott Denning
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Paul A. Dirmeyer
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Paul R. Houser
,
Guoyue Niu
,
Keith W. Oleson
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C. Adam Schlosser
, and
Zong-Liang Yang

The Common Land Model (CLM) was developed for community use by a grassroots collaboration of scientists who have an interest in making a general land model available for public use and further development. The major model characteristics include enough unevenly spaced layers to adequately represent soil temperature and soil moisture, and a multilayer parameterization of snow processes; an explicit treatment of the mass of liquid water and ice water and their phase change within the snow and soil system; a runoff parameterization following the TOPMODEL concept; a canopy photo synthesis-conductance model that describes the simultaneous transfer of CO2 and water vapor into and out of vegetation; and a tiled treatment of the subgrid fraction of energy and water balance. CLM has been extensively evaluated in offline mode and coupling runs with the NCAR Community Climate Model (CCM3). The results of two offline runs, presented as examples, are compared with observations and with the simulation of three other land models [the Biosphere-Atmosphere Transfer Scheme (BATS), Bonan's Land Surface Model (LSM), and the 1994 version of the Chinese Academy of Sciences Institute of Atmospheric Physics LSM (IAP94)].

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Reinder A. Feddes
,
Holger Hoff
,
Michael Bruen
,
Todd Dawson
,
Patricia de Rosnay
,
Paul Dirmeyer
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Robert B. Jackson
,
Pavel Kabat
,
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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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.

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Paul A. Dirmeyer
,
Jiexia Wu
,
Holly E. Norton
,
Wouter A. Dorigo
,
Steven M. Quiring
,
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
,
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
Paul A. Dirmeyer
,
Benjamin A. Cash
,
James L. Kinter III
,
Cristiana Stan
,
Thomas Jung
,
Lawrence Marx
,
Peter Towers
,
Nils Wedi
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Jennifer M. Adams
,
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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