Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Validating Climate Models with Spherical Convolutional Wasserstein Distance (2401.14657v1)

Published 26 Jan 2024 in physics.ao-ph, cs.LG, stat.AP, and stat.ML

Abstract: The validation of global climate models is crucial to ensure the accuracy and efficacy of model output. We introduce the spherical convolutional Wasserstein distance to more comprehensively measure differences between climate models and reanalysis data. This new similarity measure accounts for spatial variability using convolutional projections and quantifies local differences in the distribution of climate variables. We apply this method to evaluate the historical model outputs of the Coupled Model Intercomparison Project (CMIP) members by comparing them to observational and reanalysis data products. Additionally, we investigate the progression from CMIP phase 5 to phase 6 and find modest improvements in the phase 6 models regarding their ability to produce realistic climatologies.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (57)
  1. Global precipitation climatology project (gpcp) climate data record (cdr), version 1.3 (daily). 2020.
  2. Comparison of cmip6 and cmip5 models in simulating mean and extreme precipitation over east africa. International Journal of Climatology, 41(15):6474–6496, 2021.
  3. Can climate trends be calculated from reanalysis data? Journal of Geophysical Research: Atmospheres, 109(D11), 2004.
  4. Spherical sliced-wasserstein. arXiv preprint arXiv:2206.08780, 2022.
  5. Sliced and radon wasserstein barycenters of measures. Journal of Mathematical Imaging and Vision, 51:22–45, 2015.
  6. Permutation tests for equality of distributions of functional data. Journal of Applied Econometrics, 36(7):861–877, 2021.
  7. Evaluation and comparison of cmip6 and cmip5 model performance in simulating the seasonal extreme precipitation in the western north pacific and east asia. Weather and Climate Extremes, 31:100303, 2021.
  8. Detecting signals in fmri data using powerful fdr procedures. Statistics and its interface, 1(1):23–32, 2008.
  9. Computing fourier transforms and convolutions on the 2-sphere. Advances in applied mathematics, 15(2):202–250, 1994.
  10. Overview of the coupled model intercomparison project phase 6 (cmip6) experimental design and organization. Geoscientific Model Development, 9(5):1937–1958, 2016. doi: 10.5194/gmd-9-1937-2016. URL https://gmd.copernicus.org/articles/9/1937/2016/.
  11. Evaluation of climate models. In Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, pp.  741–866. Cambridge University Press, 2014.
  12. On a general definition of depth for functional data. 2017.
  13. Two-sample tests in functional data analysis starting from discrete data. Statistica Sinica, pp.  1511–1531, 2007.
  14. Evaluating proxy influence in assimilated paleoclimate reconstructions—testing the exchangeability of two ensembles of spatial processes. Journal of the American Statistical Association, 116(535):1100–1113, 2021.
  15. Constructing valid spatial processes on the sphere using kernel convolutions. Environmetrics, 25(1):2–15, 2014.
  16. Comparing spatial predictions. Technometrics, 53(4):414–425, 2011.
  17. The era5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730):1999–2049, 2020.
  18. Estimation of the mean of functional time series and a two-sample problem. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75(1):103–122, 2013.
  19. Generalised wendland functions for the sphere. Advances in Computational Mathematics, 49(1):3, 2023.
  20. Global precipitation at one-degree daily resolution from multisatellite observations. Journal of hydrometeorology, 2(1):36–50, 2001.
  21. A comparison of the ncep–ncar reanalysis precipitation and the gpcp rain gauge–satellite combined dataset with observational error considerations. Journal of Climate, 11(11):2960–2979, 1998.
  22. Ncep–doe amip-ii reanalysis (r-2). Bulletin of the American Meteorological Society, 83(11):1631–1644, 2002.
  23. Evaluation of historical cmip6 model simulations of seasonal mean temperature over pakistan during 1970–2014. Atmosphere, 11(9):1005, 2020.
  24. Giss-e2. 1: Configurations and climatology. Journal of Advances in Modeling Earth Systems, 12(8):e2019MS002025, 2020.
  25. Kim, J. U. Invariant measures for a stochastic nonlinear schrödinger equation. Indiana University mathematics journal, pp.  687–717, 2006.
  26. Generalized sliced wasserstein distances. Advances in neural information processing systems, 32, 2019.
  27. Defining spatial comparison metrics for evaluation of paleoclimatic field reconstructions of the common era. Environmetrics, 23(5):394–406, 2012.
  28. Comparison between spatio-temporal random processes and application to climate model data. Environmetrics, 27(5):267–279, 2016.
  29. Comparative assessment and future prediction using cmip6 and cmip5 for annual precipitation and extreme precipitation simulation. Frontiers in Earth Science, 9, 2021. ISSN 2296-6463. doi: 10.3389/feart.2021.687976. URL https://www.frontiersin.org/article/10.3389/feart.2021.687976.
  30. Differential geometric representations and algorithms for some pattern recognition and computer vision problems. Pattern Recognition Letters, 43:3–16, 2014.
  31. Functional data clustering analysis via the learning of gaussian processes with wasserstein distance. In Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23–27, 2020, Proceedings, Part II 27, pp.  393–403. Springer, 2020.
  32. Revisiting climate region definitions via clustering. Journal of Climate, 22(7):1787–1800, 2009.
  33. The seasonal cycle over the tropical pacific in coupled ocean–atmosphere general circulation models. Monthly Weather Review, 123(9):2825–2838, 1995.
  34. Revisiting sliced wasserstein on images: From vectorization to convolution. Advances in Neural Information Processing Systems, 35:17788–17801, 2022.
  35. A multiresolution gaussian process model for the analysis of large spatial datasets. Journal of Computational and Graphical Statistics, 24(2):579–599, 2015.
  36. A two sample distribution-free test for functional data with application to a diffusion tensor imaging study of multiple sclerosis. Journal of the Royal Statistical Society. Series C, Applied Statistics, 65(3):395, 2016.
  37. Spatio-temporal covariance and cross-covariance functions of the great circle distance on a sphere. Journal of the American Statistical Association, 111(514):888–898, 2016.
  38. Raäisaänen, J. How reliable are climate models? Tellus A: Dynamic Meteorology and Oceanography, 59(1):2–29, 2007.
  39. The joint ipwg/gewex precipitation assessment, 2021.
  40. Rood, R. B. Validation of Climate Models: An Essential Practice, pp. 737–762. Springer International Publishing, Cham, 2019. ISBN 978-3-319-70766-2. doi: 10.1007/978-3-319-70766-2˙30. URL https://doi.org/10.1007/978-3-319-70766-2_30.
  41. Nonparametric hypothesis testing for a spatial signal. Journal of the American Statistical Association, 97(460):1122–1140, 2002.
  42. Likelihood ratio tests for dependent data with applications to longitudinal and functional data analysis. Scandinavian Journal of Statistics, 41(4):932–949, 2014.
  43. Functional analysis: introduction to further topics in analysis, volume 4. Princeton University Press, 2011.
  44. Modelling non-euclidean movement and landscape connectivity in highly structured ecological networks. Methods in Ecology and Evolution, 6(2):169–177, 2015.
  45. Szapudi, I. Introduction to higher order spatial statistics in cosmology. In Data Analysis in Cosmology, pp.  457–492. Springer, 2008.
  46. Global precipitation measurements for validating climate models. Atmospheric Research, 197:1–20, 2017.
  47. Vallender, S. Calculation of the wasserstein distance between probability distributions on the line. Theory of Probability & Its Applications, 18(4):784–786, 1974.
  48. The wasserstein distances. Optimal Transport: Old and New, pp.  93–111, 2009.
  49. Evaluating the performance of climate models based on wasserstein distance. Geophysical Research Letters, 47(21):e2020GL089385, 2020.
  50. Functional data analysis. Annual Review of Statistics and its application, 3:257–295, 2016.
  51. Introduction to three-dimensional climate modeling. University science books, 2005.
  52. Detection of local differences in spatial characteristics between two spatiotemporal random fields. Journal of the American Statistical Association, 117(537):291–306, 2022.
  53. A comparison of cmip6 and cmip5 projections for precipitation to observational data: the case of northeastern iran. Theoretical and Applied Climatology, 142(3):1613–1623, 2020.
  54. Bcc-esm1 model datasets for the cmip6 aerosol chemistry model intercomparison project (aerchemmip). Advances in Atmospheric Sciences, 38:317–328, 2021.
  55. Statistical inferences for functional data. 2007.
  56. Two sample inference for the second-order property of temporally dependent functional data. 2015.
  57. Evaluation of the performance of cmip5 models to simulate land surface air temperature based on long-range correlation. Frontiers in Environmental Science, pp.  6, 2021.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets