14 research outputs found
Robust skill of decadal climate predictions
There is a growing need for skilful predictions of climate up to a decade ahead. Decadal climate predictions show high skill for surface temperature, but confidence in forecasts of precipitation and atmospheric circulation is much lower. Recent advances in seasonal and annual prediction show that the signal-to-noise ratio can be too small in climate models, requiring a very large ensemble to extract the predictable signal. Here, we reassess decadal prediction skill using a much larger ensemble than previously available, and reveal significant skill for precipitation over land and atmospheric circulation, in addition to surface temperature. We further propose a more powerful approach than used previously to evaluate the benefit of initialisation with observations, improving our understanding of the sources of skill. Our results show that decadal climate is more predictable than previously thought and will aid society to prepare for, and adapt to, ongoing climate variability and change.D.M.S., A.A.S., N.J.D., L.H. and R.E. were supported by the Met Office Hadley Centre
Climate Programme funded by BEIS and Defra and by the European Commission
Horizon 2020 EUCP project (GA 776613). L.P.C. was supported by the Spanish
MINECO HIATUS (CGL2015-70353-R) project. F.J.D.R. was supported by the H2020
EUCP (GA 776613) and the Spanish MINECO CLINSA (CGL2017-85791-R) projects. W.A.
M. and H.P. were supported by the German Ministry of Education and Research
(BMBF) under the project MiKlip (grant 01LP1519A). The NCAR contribution was
supported by the US National Oceanic and Atmospheric Administration (NOAA)
Climate Program Office under Climate Variability and Predictability Program Grant
NA13OAR4310138 and by the US National Science Foundation (NSF) Collaborative
Research EaSM2 Grant OCE-1243015. The NCAR contribution is also based upon work
supported by NCAR, which is a major facility sponsored by the US NSF under
Cooperative Agreement No. 1852977. The Community Earth System Model Decadal
Prediction Large Ensemble (CESM-DPLE) was generated using computational
resources provided by the US National Energy Research Scientific Computing Center,
which is supported by the Office of Science of the US Department of Energy under
Contract DE-AC02-05CH11231, as well as by an Accelerated Scientific Discovery grant
for Cheyenne (https://doi.org/10.5065/D6RX99HX) that was awarded by NCARâs
Computational and Information System Laboratory.Peer ReviewedPostprint (published version
ASTEC -- the Aarhus STellar Evolution Code
The Aarhus code is the result of a long development, starting in 1974, and
still ongoing. A novel feature is the integration of the computation of
adiabatic oscillations for specified models as part of the code. It offers
substantial flexibility in terms of microphysics and has been carefully tested
for the computation of solar models. However, considerable development is still
required in the treatment of nuclear reactions, diffusion and convective
mixing.Comment: Astrophys. Space Sci, in the pres
Presupernova Structure of Massive Stars
Issues concerning the structure and evolution of core collapse progenitor
stars are discussed with an emphasis on interior evolution. We describe a
program designed to investigate the transport and mixing processes associated
with stellar turbulence, arguably the greatest source of uncertainty in
progenitor structure, besides mass loss, at the time of core collapse. An
effort to use precision observations of stellar parameters to constrain
theoretical modeling is also described.Comment: Proceedings for invited talk at High Energy Density Laboratory
Astrophysics conference, Caltech, March 2010. Special issue of Astrophysics
and Space Science, submitted for peer review: 7 pages, 3 figure
Predictable Variations of the Carbon Sinks and Atmospheric CO<sub>2</sub> Growth in a MultiâModel Framework
International audienceInterâannual to decadal variability in the strength of the land and ocean carbon sinks impede accurate predictions of yearâtoâyear atmospheric carbon dioxide (CO2) growth rate. Such information is crucial to verify the effectiveness of fossil fuel emissions reduction measures. Using a multiâmodel framework comprising prediction systems initialized by the observed state of the physical climate, we find a predictive skill for the global ocean carbon sink of up to 6 years for some models. Longer regional predictability horizons are found across single models. On land, a predictive skill of up to 2 years is primarily maintained in the tropics and extraâtropics enabled by the initialization of the physical climate. We further show that anomalies of atmospheric CO2 growth rate inferred from natural variations of the land and ocean carbon sinks are predictable at lead time of 2 years and the skill is limited by the land carbon sink predictability horizon
Robust skill of decadal climate predictions
There is a growing need for skilful predictions of climate up to a decade ahead. Decadal climate predictions show high skill for surface temperature, but confidence in forecasts of precipitation and atmospheric circulation is much lower. Recent advances in seasonal and annual prediction show that the signal-to-noise ratio can be too small in climate models, requiring a very large ensemble to extract the predictable signal. Here, we reassess decadal prediction skill using a much larger ensemble than previously available, and reveal significant skill for precipitation over land and atmospheric circulation, in addition to surface temperature. We further propose a more powerful approach than used previously to evaluate the benefit of initialisation with observations, improving our understanding of the sources of skill. Our results show that decadal climate is more predictable than previously thought and will aid society to prepare for, and adapt to, ongoing climate variability and change.D.M.S., A.A.S., N.J.D., L.H. and R.E. were supported by the Met Office Hadley Centre
Climate Programme funded by BEIS and Defra and by the European Commission
Horizon 2020 EUCP project (GA 776613). L.P.C. was supported by the Spanish
MINECO HIATUS (CGL2015-70353-R) project. F.J.D.R. was supported by the H2020
EUCP (GA 776613) and the Spanish MINECO CLINSA (CGL2017-85791-R) projects. W.A.
M. and H.P. were supported by the German Ministry of Education and Research
(BMBF) under the project MiKlip (grant 01LP1519A). The NCAR contribution was
supported by the US National Oceanic and Atmospheric Administration (NOAA)
Climate Program Office under Climate Variability and Predictability Program Grant
NA13OAR4310138 and by the US National Science Foundation (NSF) Collaborative
Research EaSM2 Grant OCE-1243015. The NCAR contribution is also based upon work
supported by NCAR, which is a major facility sponsored by the US NSF under
Cooperative Agreement No. 1852977. The Community Earth System Model Decadal
Prediction Large Ensemble (CESM-DPLE) was generated using computational
resources provided by the US National Energy Research Scientific Computing Center,
which is supported by the Office of Science of the US Department of Energy under
Contract DE-AC02-05CH11231, as well as by an Accelerated Scientific Discovery grant
for Cheyenne (https://doi.org/10.5065/D6RX99HX) that was awarded by NCARâs
Computational and Information System Laboratory.Peer Reviewe