Improving weather predictability by including land-surface model parameter uncertainty

AbstractThe land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogeneous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model.Focusing on ECMWF’s land surface model Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the authors present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. The authors select six poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, and total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration, and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally, the authors investigate the po...

[1]  Lifeng Luo,et al.  Contribution of land surface initialization to subseasonal forecast skill: First results from a multi‐model experiment , 2010 .

[2]  Markus Reichstein,et al.  Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis , 2013 .

[3]  S. Sorooshian,et al.  Effective and efficient algorithm for multiobjective optimization of hydrologic models , 2003 .

[4]  Lifeng Luo,et al.  The Second Phase of the Global Land–Atmosphere Coupling Experiment: Soil Moisture Contributions to Subseasonal Forecast Skill , 2011 .

[5]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[6]  Warren Tennant,et al.  New schemes to perturb sea‐surface temperature and soil moisture content in MOGREPS , 2014 .

[7]  Massimiliano Zappa,et al.  Does model performance improve with complexity? : A case study with three hydrological models , 2015 .

[8]  S. Demuth,et al.  Streamflow trends in Europe: evidence from a dataset of near-natural catchments , 2010 .

[9]  F. Pappenberger,et al.  Imbalanced land surface water budgets in a numerical weather prediction system , 2015 .

[10]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[11]  Florian Pappenberger,et al.  Global sensitivity analyses for a complex hydrological model applied in an Alpine watershed , 2013 .

[12]  Jakob Zscheischler,et al.  Impact of soil moisture on extreme maximum temperatures in Europe , 2015 .

[13]  Kenneth W. Harrison,et al.  Quantifying the change in soil moisture modeling uncertainty from remote sensing observations using Bayesian inference techniques , 2012 .

[14]  P. Jones,et al.  A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006 , 2008 .

[15]  Kenneth W. Harrison,et al.  Impact of Land Model Calibration on Coupled Land–Atmosphere Prediction , 2013 .

[16]  Andrew Binley,et al.  GLUE: 20 years on , 2014 .

[17]  S. Swenson,et al.  Post‐processing removal of correlated errors in GRACE data , 2006 .

[18]  P. Cox,et al.  Detection of solar dimming and brightening effects on Northern Hemisphere river flow , 2014 .

[19]  T. N. Palmer,et al.  On the reliability of seasonal climate forecasts , 2013, Journal of The Royal Society Interface.

[20]  Mauro Sulis,et al.  The subsurface–land surface–atmosphere connection under convective conditions , 2015 .

[21]  David J. Stensrud,et al.  Surface Data Assimilation Using an Ensemble Kalman Filter Approach with Initial Condition and Model Physics Uncertainties , 2005 .

[22]  Mark M. Morrissey,et al.  The Global Precipitation Climatology Project , 2007 .

[23]  Keith Beven,et al.  Dalton Medal Lecture: How far can we go in distributed hydrological modelling? , 2001 .

[24]  Keith Beven,et al.  Distributed Hydrological Modelling , 1998 .

[25]  S. Seneviratne,et al.  Investigating soil moisture-climate interactions in a changing climate: A review , 2010 .

[26]  I. Sobol.  On the distribution of points in a cube and the approximate evaluation of integrals , 1967 .

[27]  Laure Raynaud,et al.  Sensitivity of the AROME ensemble to initial and surface perturbations during HyMeX , 2016 .

[28]  Daniel Busby,et al.  Smoothing spline analysis of variance approach for global sensitivity analysis of computer codes , 2013, Reliab. Eng. Syst. Saf..

[29]  F. Landerer,et al.  Accuracy of scaled GRACE terrestrial water storage estimates , 2012 .

[30]  Omar Bellprat,et al.  Impact of land-surface initialization on sub-seasonal to seasonal forecasts over Europe , 2016, Climate Dynamics.

[31]  Marco L. Carrera,et al.  Impact of Surface Parameter Uncertainties within the Canadian Regional Ensemble Prediction System , 2013 .

[32]  Florian Pappenberger,et al.  Multi-method global sensitivity analysis of flood inundation models. , 2008 .

[33]  G. Balsamo,et al.  The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data , 2014 .

[34]  Chong-Yu Xu,et al.  Disinformative data in large-scale hydrological modelling , 2013 .

[35]  Chris Snyder,et al.  The U.S. Air ForceWeather Agency’s mesoscale ensemble: scientific description and performance results , 2011 .

[36]  Sonia I. Seneviratne,et al.  Introduction of a simple-model-based land surface dataset for Europe , 2015 .

[37]  F. Pappenberger,et al.  Representing uncertainty in land surface hydrology: fully coupled simulations with the ECMWF land surface scheme , 2011 .

[38]  F. Pappenberger,et al.  ERA-Interim/Land: a global land surface reanalysis data set , 2015 .

[39]  Florian Pappenberger,et al.  Improved seasonal prediction of the hot summer of 2003 over Europe through better representation of uncertainty in the land surface , 2016 .

[40]  Pavel Kabat,et al.  WATCH: Current Knowledge of the Terrestrial Global Water Cycle , 2011 .

[41]  Ricardo García-Herrera,et al.  The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe , 2011, Science.

[42]  Florian Pappenberger,et al.  Multi‐method global sensitivity analysis (MMGSA) for modelling floodplain hydrological processes , 2008 .