Future climate emulations using quantile regressions on large ensembles
暂无分享,去创建一个
Ryan L. Sriver | Elisabeth J. Moyer | Matz A. Haugen | Michael L. Stein | M. Stein | E. Moyer | R. Sriver
[1] Q. Duan,et al. A nonstationary bias‐correction technique to remove bias in GCM simulations , 2016 .
[2] R. Koenker. Quantile regression for longitudinal data , 2004 .
[3] S. Girard,et al. On kernel smoothing for extremal quantile regression , 2012, 1312.5123.
[4] Marco Geraci,et al. Linear quantile mixed models , 2013, Statistics and Computing.
[5] Alex J. Cannon,et al. Multivariate Bias Correction of Climate Model Output: Matching Marginal Distributions and Intervariable Dependence Structure , 2016 .
[6] M. Fuentes,et al. Journal of the American Statistical Association Bayesian Spatial Quantile Regression Bayesian Spatial Quantile Regression , 2022 .
[7] Alex J. Cannon,et al. Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? , 2015 .
[8] Simon Jaun,et al. On interpreting hydrological change from regional climate models , 2007 .
[9] Mathieu Vrac,et al. Statistical precipitation downscaling for small-scale hydrological impact investigations of climate change , 2011 .
[10] Bala Rajaratnam,et al. The Extraordinary California Drought of 2013-2014: Character, Context, and the Role of Climate Change , 2014 .
[11] J. Christensen,et al. Overestimation of Mediterranean summer temperature projections due to model deficiencies , 2012 .
[12] Anthony C. Davison,et al. Bootstrap Methods and Their Application , 1998 .
[13] Keming Yu,et al. Bayesian quantile regression , 2001 .
[14] G. Bürger,et al. Estimates of future flow, including extremes, of the Columbia River headwaters , 2011 .
[15] Loic Le Gratiet,et al. RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY , 2012, 1210.0686.
[16] I. Watterson,et al. Calculation of probability density functions for temperature and precipitation change under global warming , 2008 .
[17] R. Sriver,et al. Analysis of ENSO’s response to unforced variability and anthropogenic forcing using CESM , 2017, Scientific Reports.
[18] C. Deser,et al. Uncertainty in climate change projections: the role of internal variability , 2012, Climate Dynamics.
[19] A. O'Hagan,et al. Predicting the output from a complex computer code when fast approximations are available , 2000 .
[20] D. Maraun. Reply to “Comment on ‘Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue’” , 2013 .
[21] J. Olsson,et al. Applying climate model precipitation scenarios for urban hydrological assessment: a case study in Kalmar City, Sweden. , 2009 .
[22] R. Koenker. Quantile Regression: Name Index , 2005 .
[23] S. Seneviratne,et al. Dependence of drivers affects risks associated with compound events , 2017, Science Advances.
[24] Matz A. Haugen,et al. Quantifying the influence of global warming on unprecedented extreme climate events , 2017, Proceedings of the National Academy of Sciences.
[25] M. C. Jones,et al. Local Linear Quantile Regression , 1998 .
[26] C. Deser,et al. Projecting North American Climate over the Next 50 Years: Uncertainty due to Internal Variability* , 2014 .
[27] B. Efron. Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods , 1981 .
[28] J. Thepaut,et al. Toward a Consistent Reanalysis of the Climate System , 2014 .
[29] E. Wood,et al. Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching , 2010 .
[30] Katharine Hayhoe,et al. A general method for validating statistical downscaling methods under future climate change , 2007 .
[31] J. Thepaut,et al. The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .
[32] J. Lanzante,et al. Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results? , 2016, Climatic Change.
[33] Christopher A. T. Ferro,et al. Global changes in extreme daily temperature since 1950 , 2008 .
[34] P. Huybers,et al. The changing shape of Northern Hemisphere summer temperature distributions , 2016 .
[35] Douglas Maraun,et al. Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums , 2012 .
[36] D. Lettenmaier,et al. Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs , 2004 .
[37] S. Schneider,et al. Emissions pathways, climate change, and impacts on California. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[38] Yufeng Liu,et al. Stepwise multiple quantile regression estimation using non-crossing constraints , 2009 .
[39] P. Friederichs,et al. Statistical Downscaling of Extreme Precipitation Events Using Censored Quantile Regression , 2007 .
[40] Lin Wang,et al. Equiratio cumulative distribution function matching as an improvement to the equidistant approach in bias correction of precipitation , 2014 .
[41] Elisabeth J. Moyer,et al. Estimating trends in the global mean temperature record , 2016, 1607.03855.
[42] K.,et al. The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability , 2015 .
[43] Lukas Gudmundsson,et al. Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods , 2012 .
[44] Alexander I. J. Forrester,et al. Multi-fidelity optimization via surrogate modelling , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[45] M. Stein,et al. Simulation of future climate under changing temporal covariance structures , 2015 .
[46] H. Bondell,et al. Noncrossing quantile regression curve estimation. , 2010, Biometrika.
[47] D. Wuebbles,et al. An asynchronous regional regression model for statistical downscaling of daily climate variables , 2013 .
[48] C. Forest,et al. Effects of initial conditions uncertainty on regional climate variability: An analysis using a low‐resolution CESM ensemble , 2015 .
[49] Xu He,et al. Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion , 2017, Technometrics.
[50] Philip T Reiss,et al. The International Journal of Biostatistics Smoothness Selection for Penalized Quantile Regression Splines , 2012 .
[51] S. Hagemann,et al. Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models , 2010 .
[52] M. Déqué,et al. Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: Model results and statistical correction according to observed values , 2007 .
[53] P. Huybers,et al. Seasonally Resolved Distributional Trends of North American Temperatures Show Contraction of Winter Variability , 2017 .
[54] W. Collins,et al. The Community Earth System Model: A Framework for Collaborative Research , 2013 .
[55] Olle Räty,et al. Projections of daily mean temperature variability in the future: cross-validation tests with ENSEMBLES regional climate simulations , 2013, Climate Dynamics.
[56] H. Kozumi,et al. Gibbs sampling methods for Bayesian quantile regression , 2011 .
[57] R. Sriver,et al. Analyzing the Effect of Ocean Internal Variability on Depth-Integrated Steric Sea-Level Rise Trends Using a Low-Resolution CESM Ensemble , 2017 .
[58] Andreas Gobiet,et al. Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal , 2012, Climatic Change.
[59] Doug Nychka,et al. A new distribution mapping technique for climate model bias correction , 2015 .
[60] R. Koenker,et al. Quantile regression methods for reference growth charts , 2006, Statistics in medicine.
[61] R. Koenker,et al. Quantile spline models for global temperature change , 1994 .
[62] A. Thomson,et al. The representative concentration pathways: an overview , 2011 .
[63] Matz A. Haugen,et al. Estimating Changes in Temperature Distributions in a Large Ensemble of Climate Simulations Using Quantile Regression , 2018, Journal of Climate.
[64] Douglas W. Nychka,et al. Fast Nonparametric Quantile Regression With Arbitrary Smoothing Methods , 2011 .
[65] Michael L. Stein,et al. Global space–time models for climate ensembles , 2013, 1311.7319.
[66] J. Seibert,et al. Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods , 2012 .