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Constantin Ardilouze, Lauriane Batté, Bertrand Decharme, and Michel Déqué

Abstract

Soil moisture anomalies are expected to be a driver of summer predictability for the U.S. Great Plains since this region is prone to intense and year-to-year varying water and energy exchange between the land and the atmosphere. However, dynamical seasonal forecast systems struggle to deliver skillful summer temperature forecasts over that region, otherwise subject to a consistent warm-season dry bias in many climate models. This study proposes two techniques to mitigate the impact of this precipitation deficit on the modeled soil water content in a forecast system based on the CNRM-CM6-1 model. Both techniques lead to increased evapotranspiration during summer and reduced temperature and precipitation bias. However, only the technique based on a correction of the precipitation feeding the land surface throughout the forecast integration enables skillful summer prediction. Although this result cannot be generalized for other parts of the globe, it confirms the link between bias and skill over the U.S. Great Plains and pleads for continued efforts of the modeling community to tackle the summer bias affecting that region.

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Augusto C. V. Getirana, Aaron Boone, Dai Yamazaki, Bertrand Decharme, Fabrice Papa, and Nelly Mognard

Abstract

Recent advances in global flow routing schemes have shown the importance of using high-resolution topography for representing floodplain inundation dynamics more reliably. This study presents and evaluates the Hydrological Modeling and Analysis Platform (HyMAP), which is a global flow routing scheme specifically designed to bridge the gap between current state-of-the-art global flow routing schemes by combining their main features and introducing new features to better capture floodplain dynamics. The ultimate goals of HyMAP are to provide the scientific community with a novel scheme suited to the assimilation of satellite altimetry data for global water discharge forecasts and a model that can be potentially coupled with atmospheric models. In this first model evaluation, HyMAP is coupled with the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface model in order to simulate the surface water dynamics in the Amazon basin. The model is evaluated over the 1986–2006 period against an unprecedented source of information, including in situ and satellite-based datasets of water discharge and level, flow velocity, and floodplain extent. Results show that the model can satisfactorily simulate the large-scale features of the water surface dynamics of the Amazon River basin. Among all stream gauges considered, 23% have Nash–Sutcliffe coefficients (NS) higher than 0.50 and 68% above zero. About 28% of the stations have volume errors lower than 15%. Simulated discharges at Óbidos had NS = 0.89. Time series of simulated floodplains at the basin scale agrees well with satellite-based estimates, with a relative error of 7% and correlation of 0.89. These results indicate nonnegligible improvements in comparison to previous studies for the same region.

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Eric Brun, Vincent Vionnet, Aaron Boone, Bertrand Decharme, Yannick Peings, Rémi Valette, Fatima Karbou, and Samuel Morin

Abstract

The Crocus snowpack model within the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface model was run over northern Eurasia from 1979 to 1993, using forcing data extracted from hydrometeorological datasets and meteorological reanalyses. Simulated snow depth, snow water equivalent, and density over open fields were compared with local observations from over 1000 monitoring sites, available either once a day or three times per month. The best performance is obtained with European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim). Provided blowing snow sublimation is taken into account, the simulations show a small bias and high correlations in terms of snow depth, snow water equivalent, and density. Local snow cover durations as well as the onset and vanishing dates of continuous snow cover are also well reproduced. A major result is that the overall performance of the simulations is very similar to the performance of existing gridded snow products, which, in contrast, assimilate local snow depth observations. Soil temperature at 20-cm depth is reasonably well simulated. The methodology developed in this study is an efficient way to evaluate different meteorological datasets, especially in terms of snow precipitation. It reveals that the temporal disaggregation of monthly precipitation in the hydrometeorological dataset from Princeton University significantly impacts the rain–snow partitioning, deteriorating the simulation of the onset of snow cover as well as snow depth throughout the cold season.

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Charlotte M. Emery, Sylvain Biancamaria, Aaron Boone, Pierre-André Garambois, Sophie Ricci, Mélanie C. Rochoux, and Bertrand Decharme

Abstract

The continental part of the water cycle is commonly represented with hydrological models. Yet, there are limits in their capacity to accurately estimate water storage and dynamics because of their coarse spatial resolution, simplified physics, and an incomplete knowledge of atmospheric forcing and input parameters. These errors can be diminished using data assimilation techniques. The model’s most sensitive parameters should be identified beforehand. The objective of the present study is to highlight key parameters impacting the river-routing scheme Total Runoff Integrating Pathways (TRIP) while simulating river water height and discharge as a function of time focusing on the annual cycle. Thus, a sensitivity analysis based on the decomposition of model output variance (using a method called ANOVA) is utilized and applied over the Amazon basin. Tested parameters are perturbed with correcting factors. First, parameter-correcting coefficients are considered uniform over the entire basin. The results are specific to the TRIP model and show that geomorphological parameters explain around 95% of the water height variance with purely additive contributions, all year long, with a dominating impact of the river Manning coefficient (40%), the riverbed slope (35%), and the river width (20%). The results also show that discharge is essentially sensitive to the groundwater time constant that makes up more than 90% of the variance. To a lesser extent, in rising/falling flow period, the discharge is also sensitive to geomorphological parameters. Next, the Amazon basin is divided into nine subregions and the sensitivity analysis is carried out for regionalized parameter-correcting coefficients. The results show that local-region parameters impact water height, while upstream-region parameters affect discharge.

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Aaron Boone, Patricia de Rosnay, Gianpaolo Balsamo, Anton Beljaars, Franck Chopin, Bertrand Decharme, Christine Delire, Agnes Ducharne, Simon Gascoin, Manuela Grippa, Françoise Guichard, Yeugeniy Gusev, Phil Harris, Lionel Jarlan, Laurent Kergoat, Eric Mougin, Olga Nasonova, Anette Norgaard, Tristan Orgeval, Catherine Ottlé, Isabelle Poccard-Leclercq, Jan Polcher, Inge Sandholt, Stephane Saux-Picart, Christopher Taylor, and Yongkang Xue

The rainfall over West Africa has been characterized by extreme variability in the last half-century, with prolonged droughts resulting in humanitarian crises. There is, therefore, an urgent need to better understand and predict the West African monsoon (WAM), because social stability in this region depends to a large degree on water resources. The economies are primarily agrarian, and there are issues related to food security and health. In particular, there is a need to better understand land-atmosphere and hydrological processes over West Africa because of their potential feedbacks with the WAM. This is being addressed through a multiscale modeling approach using an ensemble of land surface models that rely on dedicated satellite-based forcing and land surface parameter products, and data from the African Multidisciplinary Monsoon Analysis (AMMA) observational field campaigns. The AMMA land surface model (LSM) Intercomparison Project (ALMIP) offline, multimodel simulations comprise the equivalent of a multimodel reanalysis product. They currently represent the best estimate of the land surface processes over West Africa from 2004 to 2007. An overview of model intercomparison and evaluation is presented. The far-reaching goal of this effort is to obtain better understanding and prediction of the WAM and the feedbacks with the surface. This can be used to improve water management and agricultural practices over this region.

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Ned Haughton, Gab Abramowitz, Andy J. Pitman, Dani Or, Martin J. Best, Helen R. Johnson, Gianpaolo Balsamo, Aaron Boone, Matthias Cuntz, Bertrand Decharme, Paul A. Dirmeyer, Jairui Dong, Michael Ek, Zichang Guo, Vanessa Haverd, Bart J. J. van den Hurk, Grey S. Nearing, Bernard Pak, Joe A. Santanello Jr., Lauren E. Stevens, and Nicolas Vuichard

Abstract

The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) illustrated the value of prescribing a priori performance targets in model intercomparisons. It showed that the performance of turbulent energy flux predictions from different land surface models, at a broad range of flux tower sites using common evaluation metrics, was on average worse than relatively simple empirical models. For sensible heat fluxes, all land surface models were outperformed by a linear regression against downward shortwave radiation. For latent heat flux, all land surface models were outperformed by a regression against downward shortwave radiation, surface air temperature, and relative humidity. These results are explored here in greater detail and possible causes are investigated. It is examined whether particular metrics or sites unduly influence the collated results, whether results change according to time-scale aggregation, and whether a lack of energy conservation in flux tower data gives the empirical models an unfair advantage in the intercomparison. It is demonstrated that energy conservation in the observational data is not responsible for these results. It is also shown that the partitioning between sensible and latent heat fluxes in LSMs, rather than the calculation of available energy, is the cause of the original findings. Finally, evidence is presented that suggests that the nature of this partitioning problem is likely shared among all contributing LSMs. While a single candidate explanation for why land surface models perform poorly relative to empirical benchmarks in PLUMBER could not be found, multiple possible explanations are excluded and guidance is provided on where future research should focus.

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Françoise Guichard, Nicole Asencio, Christophe Peugeot, Olivier Bock, Jean-Luc Redelsperger, Xuefeng Cui, Matthew Garvert, Benjamin Lamptey, Emiliano Orlandi, Julia Sander, Federico Fierli, Miguel Angel Gaertner, Sarah C. Jones, Jean-Philippe Lafore, Andrew Morse, Mathieu Nuret, Aaron Boone, Gianpaolo Balsamo, Patricia de Rosnay, Bertrand Decharme, Philip P. Harris, and J.-C. Bergès

Abstract

An evaluation of precipitation and evapotranspiration simulated by mesoscale models is carried out within the African Monsoon Multidisciplinary Analysis (AMMA) program. Six models performed simulations of a mesoscale convective system (MCS) observed to cross part of West Africa in August 2005.

Initial and boundary conditions are found to significantly control the locations of rainfall at synoptic scales as simulated with either mesoscale or global models. When initialized and forced at their boundaries by the same analysis, all models forecast a westward-moving rainfall structure, as observed by satellite products. However, rainfall is also forecast at other locations where none was observed, and the nighttime northward propagation of rainfall is not well reproduced. There is a wide spread in the rainfall rates across simulations, but also among satellite products.

The range of simulated meridional fluctuations of evapotranspiration (E) appears reasonable, but E displays an overly strong zonal symmetry. Offline land surface modeling and surface energy budget considerations show that errors in the simulated E are not simply related to errors in the surface evaporative fraction, and involve the significant impact of cloud cover on the incoming surface shortwave flux.

The use of higher horizontal resolution (a few km) enhances the variability of precipitation, evapotranspiration, and precipitable water (PW) at the mesoscale. It also leads to a weakening of the daytime precipitation, less evapotranspiration, and smaller PW amounts. The simulated MCS propagates farther northward and somewhat faster within an overall drier atmosphere. These changes are associated with a strengthening of the links between PW and precipitation.

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Augusto C. V. Getirana, Emanuel Dutra, Matthieu Guimberteau, Jonghun Kam, Hong-Yi Li, Bertrand Decharme, Zhengqiu Zhang, Agnes Ducharne, Aaron Boone, Gianpaolo Balsamo, Matthew Rodell, Ally M. Toure, Yongkang Xue, Christa D. Peters-Lidard, Sujay V. Kumar, Kristi Arsenault, Guillaume Drapeau, L. Ruby Leung, Josyane Ronchail, and Justin Sheffield

Abstract

Despite recent advances in land surface modeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-of-the-art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 1° spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to match monthly Global Precipitation Climatology Project (GPCP) and Global Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l’Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets and Gravity Recovery and Climate Experiment (GRACE) TWS estimates in two subcatchments of main tributaries (Madeira and Negro Rivers). At the basin scale, simulated ET ranges from 2.39 to 3.26 mm day−1 and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.

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Cecile B. Menard, Richard Essery, Gerhard Krinner, Gabriele Arduini, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Emanuel Dutra, Xing Fang, Charles Fierz, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Hyungjun Kim, Matthieu Lafaysse, Thomas Marke, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Gerd Schädler, Vladimir A. Semenov, Tatiana Smirnova, Ulrich Strasser, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan

Abstract

Twenty-seven models participated in the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modeling. Our findings do not support the hypothesis advanced by previous snow MIPs: evaluating models against more variables and providing evaluation datasets extended temporally and spatially does not facilitate identification of key new processes requiring improvement to model snow mass and energy budgets, even at point scales. In fact, the same modeling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parameterizations are problematic, and individual model performance is inconsistent. This lack of progress is attributed partly to the large number of human errors that led to anomalous model behavior and to numerous resubmissions. It is unclear how widespread such errors are in our field and others; dedicated time and resources will be needed to tackle this issue to prevent highly sophisticated models and their research outputs from being vulnerable because of avoidable human mistakes. The design of and the data available to successive snow MIPs were also questioned. Evaluation of models against bulk snow properties was found to be sufficient for some but inappropriate for more complex snow models whose skills at simulating internal snow properties remained untested. Discussions between the authors of this paper on the purpose of MIPs revealed varied, and sometimes contradictory, motivations behind their participation. These findings started a collaborative effort to adapt future snow MIPs to respond to the diverse needs of the community.

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