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  • Author or Editor: Paul A. Dirmeyer x
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Ned Haughton
,
Gab Abramowitz
,
Andy J. Pitman
,
Dani Or
,
Martin J. Best
,
Helen R. Johnson
,
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
,
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
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
Randal D. Koster
,
Y. C. Sud
,
Zhichang Guo
,
Paul A. Dirmeyer
,
Gordon Bonan
,
Keith W. Oleson
,
Edmond Chan
,
Diana Verseghy
,
Peter Cox
,
Harvey Davies
,
Eva Kowalczyk
,
C. T. Gordon
,
Shinjiro Kanae
,
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 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.

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