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  • Author or Editor: Paul A. Dirmeyer x
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Paul A. Dirmeyer
,
A. J. Dolman
, and
Nobuo Sato

The Global Soil Wetness Project (GSWP) is an ongoing land surface modeling activity of the International Satellite Land-Surface Climatology Project (ISLSCP), a part of the Global Energy and Water Cycle Experiment. The pilot phase of GSWP deals with the production of a two-year global dataset of soil moisture, temperature, runoff, and surface fluxes by integrating uncoupled land surface schemes (LSSs) using externally specified surface forcings from observations and standardized soil and vegetation distributions. Approximately one dozen participating LSS groups in five nations have taken the common ISLSCP forcing data to drive their state-of-the-art models over the 1987–88 period to generate global datasets. Many of the LSS groups have performed specific sensitivity studies, which are intended to evaluate the impact of uncertainties in model parameters and forcing fields on simulation of the surface water and energy balances. A validation effort exists to compare the global products to other forms of estimation and measurement, either directly (by comparison to field studies or soil moisture measuring networks) or indirectly (e.g., use of modeled runoff to drive river routing schemes for comparison to streamflow data). The soil wetness data produced are also being tested within general circulation models to evaluate their quality and their impact on seasonal to interannual climate simulations. An Inter-Comparison Center has also been established for evaluating and comparing data from the different LSSs. Comparison among the model results is used to assess the uncertainty in estimates of surface components of the moisture and energy balances at large scales and as a quality check on the model products themselves.

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Paul A. Dirmeyer
,
Xiang Gao
,
Mei Zhao
,
Zhichang Guo
,
Taikan Oki
, and
Naota Hanasaki

Quantification of sources and sinks of carbon at global and regional scales requires not only a good description of the land sources and sinks of carbon, but also of the synoptic and mesoscale meteorology. An experiment was performed in Les Landes, southwest France, during May–June 2005, to determine the variability in concentration gradients and fluxes of CO2 The CarboEurope Regional Experiment Strategy (CERES; see also http://carboregional.mediasfrance.org/index) aimed to produce aggregated estimates of the carbon balance of a region that can be meaningfully compared to those obtained from the smallest downscaled information of atmospheric measurements and continental-scale inversions. We deployed several aircraft to sample the CO2 concentration and fluxes over the whole area, while fixed stations observed the fluxes and concentrations at high accuracy. Several (mesoscale) meteorological modeling tools were used to plan the experiment and flight patterns.

Results show that at regional scale the relation between profiles and fluxes is not obvious, and is strongly influenced by airmass history and mesoscale flow patterns. In particular, we show from an analysis of data for a single day that taking either the concentration at several locations as representative of local fluxes or taking the flux measurements at those sites as representative of larger regions would lead to incorrect conclusions about the distribution of sources and sinks of carbon. Joint consideration of the synoptic and regional flow, fluxes, and land surface is required for a correct interpretation. This calls for an experimental and modeling strategy that takes into account the large spatial gradients in concentrations and the variability in sources and sinks that arise from different land use types. We briefly describe how such an analysis can be performed and evaluate the usefulness of the data for planning of future networks or longer campaigns with reduced experimental efforts.

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Paul A. Dirmeyer
,
Xiang Gao
,
Mei Zhao
,
Zhichang Guo
,
Taikan Oki
, and
Naota Hanasaki
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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Yongjiu Dai
,
Xubin Zeng
,
Robert E. Dickinson
,
Ian Baker
,
Gordon B. Bonan
,
Michael G. Bosilovich
,
A. Scott Denning
,
Paul A. Dirmeyer
,
Paul R. Houser
,
Guoyue Niu
,
Keith W. Oleson
,
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
,
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.

Full access
Annarita Mariotti
,
Cory Baggett
,
Elizabeth A. Barnes
,
Emily Becker
,
Amy Butler
,
Dan C. Collins
,
Paul A. Dirmeyer
,
Laura Ferranti
,
Nathaniel C. Johnson
,
Jeanine Jones
,
Ben P. Kirtman
,
Andrea L. Lang
,
Andrea Molod
,
Matthew Newman
,
Andrew W. Robertson
,
Siegfried Schubert
,
Duane E. Waliser
, and
John Albers

Abstract

There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer time scales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden–Julian oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, reevaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.

Free access
Annarita Mariotti
,
Cory Baggett
,
Elizabeth A. Barnes
,
Emily Becker
,
Amy Butler
,
Dan C. Collins
,
Paul A. Dirmeyer
,
Laura Ferranti
,
Nathaniel C. Johnson
,
Jeanine Jones
,
Ben P. Kirtman
,
Andrea L. Lang
,
Andrea Molod
,
Matthew Newman
,
Andrew W. Robertson
,
Siegfried Schubert
,
Duane E. Waliser
, and
John Albers
Full access
William J. Merryfield
,
Johanna Baehr
,
Lauriane Batté
,
Emily J. Becker
,
Amy H. Butler
,
Caio A. S. Coelho
,
Gokhan Danabasoglu
,
Paul A. Dirmeyer
,
Francisco J. Doblas-Reyes
,
Daniela I. V. Domeisen
,
Laura Ferranti
,
Tatiana Ilynia
,
Arun Kumar
,
Wolfgang A. Müller
,
Michel Rixen
,
Andrew W. Robertson
,
Doug M. Smith
,
Yuhei Takaya
,
Matthias Tuma
,
Frederic Vitart
,
Christopher J. White
,
Mariano S. Alvarez
,
Constantin Ardilouze
,
Hannah Attard
,
Cory Baggett
,
Magdalena A. Balmaseda
,
Asmerom F. Beraki
,
Partha S. Bhattacharjee
,
Roberto Bilbao
,
Felipe M. de Andrade
,
Michael J. DeFlorio
,
Leandro B. Díaz
,
Muhammad Azhar Ehsan
,
Georgios Fragkoulidis
,
Sam Grainger
,
Benjamin W. Green
,
Momme C. Hell
,
Johnna M. Infanti
,
Katharina Isensee
,
Takahito Kataoka
,
Ben P. Kirtman
,
Nicholas P. Klingaman
,
June-Yi Lee
,
Kirsten Mayer
,
Roseanna McKay
,
Jennifer V. Mecking
,
Douglas E. Miller
,
Nele Neddermann
,
Ching Ho Justin Ng
,
Albert Ossó
,
Klaus Pankatz
,
Simon Peatman
,
Kathy Pegion
,
Judith Perlwitz
,
G. Cristina Recalde-Coronel
,
Annika Reintges
,
Christoph Renkl
,
Balakrishnan Solaraju-Murali
,
Aaron Spring
,
Cristiana Stan
,
Y. Qiang Sun
,
Carly R. Tozer
,
Nicolas Vigaud
,
Steven Woolnough
, and
Stephen Yeager
Full access
William J. Merryfield
,
Johanna Baehr
,
Lauriane Batté
,
Emily J. Becker
,
Amy H. Butler
,
Caio A. S. Coelho
,
Gokhan Danabasoglu
,
Paul A. Dirmeyer
,
Francisco J. Doblas-Reyes
,
Daniela I. V. Domeisen
,
Laura Ferranti
,
Tatiana Ilynia
,
Arun Kumar
,
Wolfgang A. Müller
,
Michel Rixen
,
Andrew W. Robertson
,
Doug M. Smith
,
Yuhei Takaya
,
Matthias Tuma
,
Frederic Vitart
,
Christopher J. White
,
Mariano S. Alvarez
,
Constantin Ardilouze
,
Hannah Attard
,
Cory Baggett
,
Magdalena A. Balmaseda
,
Asmerom F. Beraki
,
Partha S. Bhattacharjee
,
Roberto Bilbao
,
Felipe M. de Andrade
,
Michael J. DeFlorio
,
Leandro B. Díaz
,
Muhammad Azhar Ehsan
,
Georgios Fragkoulidis
,
Alex O. Gonzalez
,
Sam Grainger
,
Benjamin W. Green
,
Momme C. Hell
,
Johnna M. Infanti
,
Katharina Isensee
,
Takahito Kataoka
,
Ben P. Kirtman
,
Nicholas P. Klingaman
,
June-Yi Lee
,
Kirsten Mayer
,
Roseanna McKay
,
Jennifer V. Mecking
,
Douglas E. Miller
,
Nele Neddermann
,
Ching Ho Justin Ng
,
Albert Ossó
,
Klaus Pankatz
,
Simon Peatman
,
Kathy Pegion
,
Judith Perlwitz
,
G. Cristina Recalde-Coronel
,
Annika Reintges
,
Christoph Renkl
,
Balakrishnan Solaraju-Murali
,
Aaron Spring
,
Cristiana Stan
,
Y. Qiang Sun
,
Carly R. Tozer
,
Nicolas Vigaud
,
Steven Woolnough
, and
Stephen Yeager

Abstract

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

Free access