×

The physics of numerical analysis: a climate modelling case study. (English) Zbl 1462.86010

Summary: The case is made for a much closer synergy between climate science, numerical analysis and computer science.

MSC:

86A08 Climate science and climate modeling
86-08 Computational methods for problems pertaining to geophysics

References:

[1] IPCC. 2013 Climate change 2013: the physical science basis. In Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change (eds Stocker TF et al.), 1535 p. Cambridge, UK, New York, NY: Cambridge University Press.
[2] Palmer TN, Stevens B. 2019 Is climate science doing its part to address the challenge of climate change? Proc. Natl Acad. Sci. USA 116, 24 390-24 395. (doi:10.1073/pnas.1906691116) · doi:10.1073/pnas.1906691116
[3] Palmer TN. 2016 A personal perspective on modelling the climate system. Proc. R. Soc. Lond. A Math. Phys. Sci. 472, 20150772. (doi:10.1098/rspa.2015.0772) · doi:10.1098/rspa.2015.0772
[4] Palmer TN. 2011 A CERN for climate change. Phys. World 24, 14-15. (doi:10.1088/2058-7058/24/03/24) · doi:10.1088/2058-7058/24/03/24
[5] Arakawa A. 2004 The cumulus parameterization problem: past, present, and future. J. Clim. 17, 2493-2525. (doi:10.1175/1520-0442(2004)017<2493:RATCPP>2.0.CO;2) · doi:10.1175/1520-0442(2004)017<2493:RATCPP>2.0.CO;2
[6] Majda AJ, Bertozzi AL. 2001 Vorticity and incompressible flow. Cambridge texts in applied mathematics. Cambridge, UK: Cambridge University Press.
[7] Nastrom GD, Gage KS. 1985 A climatology of atmospheric wavenumber spectra observed by commercial aircraft. J. Atmos. Sci. 42, 950-960. (doi:10.1175/1520-0469(1985)042<0950:ACOAWS>2.0.CO;2) · doi:10.1175/1520-0469(1985)042<0950:ACOAWS>2.0.CO;2
[8] Lovejoy S, Schertzer D. 2013. The weather and climate - emergent laws and multifractal cascades. Cambridge, UK: Cambridge University Press. Cambridge. · Zbl 1378.86002
[9] Buizza R, Miller MJ, Palmer TN. 1999 Stochastic simulation of model uncertainties in the ECMWF Ensemble Prediction System. Q. J. R. Meteorol. Soc. 125, 2887-2908. (doi:10.1002/qj.49712556006) · doi:10.1002/qj.49712556006
[10] Palmer TN. 2001 A nonlinear dynamical perspective on model error: a proposal for nonlocal stochastic-dynamic parametrisation in weather and climate prediction models. Q. J. R. Meteorol. Soc. 127, 279-304. (doi:10.1002/qj.49712757202) · doi:10.1002/qj.49712757202
[11] Palmer TN. 2019 Stochastic weather and climate models. Nat. Phys. Rev. 1, 463-471.
[12] Weisheimer A, Corti S, Palmer T, Vitart F. 2014 Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system. Phil. Trans. A Math. Phys. Eng. Sci. 372, 20130290. (doi:10.1098/rsta.2013.0290) · Zbl 1353.86028 · doi:10.1098/rsta.2013.0290
[13] Christensen HM, Berner J, Coleman DRB, Palmer TN. 2017 Stochastic parameterization and El Niño-southern oscillation. J. Clim. 30, 17-38. (doi:10.1175/JCLI-D-16-0122.1) · doi:10.1175/JCLI-D-16-0122.1
[14] Berner J, Achatz U, Batte L, Bengtsson L, de la Camara A, Christensen HM, Yano JI. 2017 Stochastic parameterization toward a new view of weather and climate models. Bull. Am. Meteorol. Soc. 98, 565-587. (doi:10.1175/BAMS-D-15-00268.1) · doi:10.1175/BAMS-D-15-00268.1
[15] Palmer T, Düben P, McNamara H. 2014 Stochastic modelling and energy-efficient computing for weather and climate prediction. Phil. Trans. A Math. Phys. Eng. Sci. 372, 20140118. (doi:10.1098/rsta.2014.0118) · Zbl 1353.00014 · doi:10.1098/rsta.2014.0118
[16] Palem KV. 2014 Inexactness and a future of computing. Phil. Trans. R. Soc. A 372, 20130281. (doi:10.1098/rsta.2013.0281) · Zbl 1353.68078 · doi:10.1098/rsta.2013.0281
[17] Dawson A, Düben PD. 2017 An emulator for reduced floating-point precision in large numerical simulations. Geosci. Model Dev. 10, 2221-2230. (doi:10.5194/gmd-10-2221-2017) · doi:10.5194/gmd-10-2221-2017
[18] Düben PD, Palmer TN. 2014 Benchmark tests for numerical weather forecasts on inexact hardware. Mon. Weather Rev. 142, 3809-3829. (doi:10.1175/MWR-D-14-00110.1) · doi:10.1175/MWR-D-14-00110.1
[19] Thornes T, Düben PD, Palmer TN. 2018 A power law for reduced precision at small spatial scales: experiments with an SQG model. Q. J. R. Meteorol. Soc. 144, 1179-1188. (doi:10.1002/qj.3303) · doi:10.1002/qj.3303
[20] Chantry M, Thornes T, Palmer T, Dueben P. 2019 Scale-selective precision for weather and climate forecasting. Mon. Weather Rev. 147, 645-655. (doi:10.1175/MWR-D-18-0308.1) · doi:10.1175/MWR-D-18-0308.1
[21] Saffin L. Submitted. Using stochastic physics to determine the required numerical precision for the parametrization schemes of a global atmospheric model.
[22] Vána F, Düben PD, Lang S, Palmer TN, Leutbecher M, Salmond D, Carver G. 2017 Single precision in weather forecasting models: an evaluation with the IFS. Mon. Weather Rev. 145, 495-502. (doi:10.1175/MWR-D-16-0228.1) · doi:10.1175/MWR-D-16-0228.1
[23] Higham NJ, Pranesh S, Zounon M. 2019 Squeezing a matrix into half precision, with an application to solving linear systems. SIAM J. Sci. Comput. 41, A2536-A2551. (doi:10.1137/18M1229511) · Zbl 1420.65017 · doi:10.1137/18M1229511
[24] Chevallier F, Chéruy F, Scott NA, Chédin A. 1998 A neural network approach for a fast and accurate computation of longwave radiative budget. J. Appl. Meteorol. 37, 1385-1397. (doi:10.1175/1520-0450(1998)037<1385:ANNAFA>2.0.CO;2) · doi:10.1175/1520-0450(1998)037<1385:ANNAFA>2.0.CO;2
[25] Krasnopolsky VM. 2013 The application of neural networks in the Earth-System sciences. In Atmospheric and oceanographic sciences library, 46. Berlin, Germany: Springer.
[26] Hohenegger C, Kornblueh L, Klocke D, Becker T, Cioni G, Engels JF, Schulzweide U, Stevens B. In press. Climate statistics in global simulations of the atmosphere, from 80 to 2.5 km spacing. J. R. Meteorol. Soc. Jpn. (doi:10.2151/jmsj.2020-005) · doi:10.2151/jmsj.2020-005
[27] Temprado J-V, Ben N, Panosetti D, Schlemmer L, Schaer C. In press. Climate models permit convection at much coarser resolutions than previously considered. J. Clim. (https://doi.org/10.1175/JCLI-D-19-0286.1) · doi:10.1175/JCLI-D-19-0286.1
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.