A simple groundwater scheme in the TRIP river routing model: global off-line evaluation against GRACE terrestrial water storage estimates and observed river discharges

Groundwater is a non-negligible component of the global hydrological cycle, and its interaction with overlying unsaturated zones can influence water and energy fluxes between the land surface and the atmosphere. Despite its importance, groundwater is not yet represented in most climate models. In this paper, the simple groundwater scheme implemented in the Total Runoff Integrating Pathways (TRIP) river routing model is applied in off-line mode at global scale using a 0.5° model resolution. The simulated river discharges are evaluated against a large dataset of about 3500 gauging stations compiled from the Global Data Runoff Center (GRDC) and other sources, while the terrestrial water storage (TWS) variations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission help to evaluate the simulated TWS. The forcing fields (surface runoff and deep drainage) come from an independent simulation of the Interactions between Soil-Biosphere-Atmosphere (ISBA) land surface model covering the period from 1950 to 2008. Results show that groundwater improves the efficiency scores for about 70% of the gauging stations and deteriorates them for 15%. The simulated TWS are also in better agreement with the GRACE estimates. These results are mainly explained by the lag introduced by the low-frequency variations of groundwater, which tend to shift and smooth the simulated river discharges and TWS. A sensitivity study on the global precipitation forcing used in ISBA to produce the forcing fields is also proposed. It shows that the groundwater scheme is not influenced by the uncertainties in precipitation data

Past terrestrial water storage (1980–2008) in the Amazon Basin reconstructed from GRACE and in situ river gauging data

Terrestrial water storage (TWS) composed of surface waters, soil moisture, groundwater and snow where appropriate, is a key element of global and continental water cycle. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) space gravimetry mission provides a new tool to measure large-scale TWS variations. However, for the past few decades, direct estimate of TWS variability is accessible from hydrological modeling only. Here we propose a novel approach that combines GRACE-based TWS spatial patterns with multi-decadal-long in situ river level records, to reconstruct past 2-D TWS over a river basin. Results are presented for the Amazon Basin for the period 1980–2008, focusing on the interannual time scale. Results are compared with past TWS estimated by the global hydrological model ISBA-TRIP. Correlations between reconstructed past interannual TWS variability and known climate forcing modes over the region (e.g., El Niño-Southern Oscillation and Pacific Decadal Oscillation) are also estimated. This method offers new perspective for improving our knowledge of past interannual TWS in world river basins where natural climate variability (as opposed to direct anthropogenic forcing) drives TWS variations

A New Land Surface Hydrology within the Noah-WRF Land-Atmosphere Mesoscale Model Applied to Semiarid Environment: Evaluation over the Dantiandou Kori (Niger)

Land-atmosphere feedbacks, which are particularly important over the Sahel during the West African Monsoon (WAM), partly depend on a large range of processes linked to the land surface hydrology and the vegetation heterogeneities. This study focuses on the evaluation of a new land surface hydrology within the Noah-WRF land-atmosphere-coupled mesoscale model over the Sahel. This new hydrology explicitly takes account for the Dunne runoff using topographic information, the Horton runoff using a Green-Ampt approximation, and land surface heterogeneities. The previous and new versions of Noah-WRF are compared against a unique observation dataset located over the Dantiandou Kori (Niger). This dataset includes dense rain gauge network, surfaces temperatures estimated from MSG/SEVIRI data, surface soil moisture mapping based on ASAR/ENVISAT C-band radar data and in situ observations of surface atmospheric and land surface energy budget variables. Generally, the WAM is reasonably reproduced by Noah-WRF even if some limitations appear throughout the comparison between simulations and observations. An appreciable improvement of the model results is also found when the new hydrology is used. This fact seems to emphasize the relative importance of the representation of the land surface hydrological processes on the WAM simulated by Noah-WRF over the Sahel

A New Process-Based Soil Methane Scheme:Evaluation Over Arctic Field Sites With the ISBA Land Surface Model

Permafrost soils and arctic wetlands methane emissions represent an important challenge for modeling the future climate. Here we present a process-based model designed to correctly represent the main thermal, hydrological, and biogeochemical processes related to these emissions for general land surface modeling. We propose a new multilayer soil carbon and gas module within the Interaction Soil-Biosphere-Atmosphere (ISBA) land-surface model (LSM). This module represents carbon pools, vertical carbon dynamics, and both oxic and anoxic organic matter decomposition. It also represents the soil gas processes for CH4, CO2, and O2 through the soil column. We base CH4 production and oxydation on an O2 control instead of the classical water table level strata approach used in state-of-the-art soil CH4 models. We propose a new parametrization of CH4 oxydation using recent field experiments and use an explicit O2 limitation for soil carbon decomposition. Soil gas transport is computed explicitly, using a revisited formulation of plant-mediated transport, a new representation of gas bulk diffusivity in porous media closer to experimental observations, and an innovative advection term for ebullition. We evaluate this advanced model on three climatically distinct sites : two in Greenland (Nuuk and Zackenberg) and one in Siberia (Chokurdakh). The model realistically reproduces methane and carbon dioxide emissions from both permafrosted and nonpermafrosted sites. The evolution and vertical characteristics of the underground processes leading to these fluxes are consistent with current knowledge. Results also show that physics is the main driver of methane fluxes, and the main source of variability appears to be the water table depth

Characterizing, modelling and understanding the climate variability of the deep water formation in the North-Western Mediterranean Sea

Observing, modelling and understanding the climate-scale variability of the deep water formation (DWF) in the North-Western Mediterranean Sea remains today very challenging. In this study, we first characterize the interannual variability of this phenomenon by a thorough reanalysis of observations in order to establish reference time series. These quantitative indicators include 31 observed years for the yearly maximum mixed layer depth over the period 1980–2013 and a detailed multi-indicator description of the period 2007–2013. Then a 1980–2013 hindcast simulation is performed with a fully-coupled regional climate system model including the high-resolution representation of the regional atmosphere, ocean, land-surface and rivers. The simulation reproduces quantitatively well the mean behaviour and the large interannual variability of the DWF phenomenon. The model shows convection deeper than 1000 m in 2/3 of the modelled winters, a mean DWF rate equal to 0.35 Sv with maximum values of 1.7 (resp. 1.6) Sv in 2013 (resp. 2005). Using the model results, the winter-integrated buoyancy loss over the Gulf of Lions is identified as the primary driving factor of the DWF interannual variability and explains, alone, around 50 % of its variance. It is itself explained by the occurrence of few stormy days during winter. At daily scale, the Atlantic ridge weather regime is identified as favourable to strong buoyancy losses and therefore DWF, whereas the positive phase of the North Atlantic oscillation is unfavourable. The driving role of the vertical stratification in autumn, a measure of the water column inhibition to mixing, has also been analyzed. Combining both driving factors allows to explain more than 70 % of the interannual variance of the phenomenon and in particular the occurrence of the five strongest convective years of the model (1981, 1999, 2005, 2009, 2013). The model simulates qualitatively well the trends in the deep waters (warming, saltening, increase in the dense water volume, increase in the bottom water density) despite an underestimation of the salinity and density trends. These deep trends come from a heat and salt accumulation during the 1980s and the 1990s in the surface and intermediate layers of the Gulf of Lions before being transferred stepwise towards the deep layers when very convective years occur in 1999 and later. The salinity increase in the near Atlantic Ocean surface layers seems to be the external forcing that finally leads to these deep trends. In the future, our results may allow to better understand the behaviour of the DWF phenomenon in Mediterranean Sea simulations in hindcast, forecast, reanalysis or future climate change scenario modes. The robustness of the obtained results must be however confirmed in multi-model studies

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

Snow cover duration trends observed at sites and predicted by multiple models

The 30-year simulations of seasonal snow cover in 22 physically based models driven with bias-corrected meteorological reanalyses are examined at four sites with long records of snow observations. Annual snow cover durations differ widely between models, but interannual variations are strongly correlated because of the common driving data. No significant trends are observed in starting dates for seasonal snow cover, but there are significant trends towards snow cover ending earlier at two of the sites in observations and most of the models. A simplified model with just two parameters controlling solar radiation and sensible heat contributions to snowmelt spans the ranges of snow cover durations and trends. This model predicts that sites where snow persists beyond annual peaks in solar radiation and air temperature will experience rapid decreases in snow cover duration with warming as snow begins to melt earlier and at times of year with more energy available for melting

The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of Earth surface variables and fluxes

CC Attribution 3.0 License.Final revised paper also available at http://www.geosci-model-dev.net/6/929/2013/gmd-6-929-2013.pdfInternational audienceSURFEX is a new externalized land and ocean surface platform that describes the surface fluxes and the evolution of four types of surface: nature, town, inland water and ocean. It can be run either coupled or in offline mode. It is mostly based on pre-existing, well validated scientific models. It can be used in offline mode (from point scale to global runs) or fully coupled with an atmospheric model. SURFEX is able to simulate fluxes of carbon dioxide, chemical species, continental aerosols, sea salt and snow particles. It also includes a data assimilation module. The main principles of the organization of the surface are described first. Then, a survey is made of the scientific module (including the coupling strategy). Finally the main applications of the code are summarized. The current applications are extremely diverse, ranging from surface monitoring and hydrology to numerical weather prediction and global climate simulations. The validation work undertaken shows that replacing the pre-existing surface models by SURFEX in these applications is usually associated with improved skill, as the numerous scientific developments contained in this community code are used to good advantage

A global water resources ensemble of hydrological models: The eartH2Observe Tier-1 dataset

The dataset presented here consists of an ensemble of 10 global hydrological and land surface models for the period 1979–2012 using a reanalysis-based meteorological forcing dataset (0.5° resolution). The current dataset serves as a state of the art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i) snow-dominated regions and (ii) tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB) showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates. The results also show that there is currently no single best model for all variables and that model performance is spatially variable. In our unconstrained model runs the ensemble mean of total runoff into the ocean was 46 268 km3 yr−1 (334 kg m−2 yr−1), while the ensemble mean of total evaporation was 537 kg m−2 yr−1.This research received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 603608, “Global Earth Observation for integrated water resource assessment”: eartH2Observ