9 research outputs found
Scientific and human errors in a snow model intercomparison
International audienceTwenty-seven models participated in the Earth System Model - Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modelling. 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 modelling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parametrizations 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 behaviour 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 for15 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 communit
The AMMA Land Surface Model Intercomparison Project (ALMIP)
International audienceThe 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 landâatmosphere 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
Validation of the energy budget of an alpine snowpack simulated by several snow models (SnowMIP project)
Many snow models have been developed for various applications such as
hydrology, global atmospheric circulation models and avalanche forecasting. The degree
of complexity of these models is highly variable, ranging from simple index methods to
multi-layer models that simulate snow-cover stratigraphy and texture. In the framework
of the SnowModel Intercomparison Project (SnowMIP), 23 modelswere compared using
observedmeteorological parameters fromtwo mountainous alpine sites.The analysis here
focuses on validation of snow energy-budget simulations. Albedo and snow surface temperature
observations allow identification of the more realistic simulations and quantification
of errors for two components of the energy budget: the net short- and longwave
radiation. In particular, the different albedo parameterizations are evaluated for different
snowpack states (in winter and spring). Analysis of results during the melting period
allows an investigation of the different ways of partitioning the energy fluxes and reveals
the complex feedbacks which occur when simulating the snow energy budget. Particular
attention is paid to the impact of model complexity on the energy-budget components.
The model complexity has a major role for the net longwave radiation calculation,
whereas the albedo parameterization is the most significant factor explaining the accuracy
of the net shortwave radiation simulation
Evaluation of forest snow processes models (SnowMIP2)
Thirtyâthree snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up to two winter snow seasons. Modeled estimates of snow water equivalent (SWE) or depth were compared to observations at forest and open sites at each location. Precipitation phase and duration of aboveâfreezing air temperatures are shown to be major influences on divergence and convergence of modeled estimates of the subcanopy snowpack. When models are considered collectively at all locations, comparisons with observations show that it is harder to model SWE at forested sites than open sites. There is no universal âbestâ model for all sites or locations, but comparison of the consistency of individual model performances relative to one another at different sites shows that there is less consistency at forest sites than open sites, and even less consistency between forest and open sites in the same year. A good performance by a model at a forest site is therefore unlikely to mean a good model performance by the same model at an open site (and vice versa). Calibration of models at forest sites provides lower errors than uncalibrated models at three out of four locations. However, benefits of calibration do not translate to subsequent years, and benefits gained by models calibrated for forest snow processes are not translated to open conditions