Assessing the role of Mediterranean evergreen oaks canopy cover in land surface albedo and temperature using a remote sensing-based approach

Modifications in vegetation cover can have an impact on the climate through changes in biogeochemical and biogeophysical processes. In this paper, the tree canopy cover percentage of a savannah-like ecosystem (montado/dehesa) was estimated at Landsat pixel level for 2011, and the role of different canopy cover percentages on land surface albedo (LSA) and land surface temperature (LST) were analysed. A modelling procedure using a SGB machine-learning algorithm and Landsat 5-TM spectral bands and derived vegetation indices as explanatory variables, showed that the estimation of montado canopy cover was obtained with good agreement (R2 = 78.4%). Overall, montado canopy cover estimations showed that low canopy cover class (MT_1) is the most representative with 50.63% of total montado area. MODIS LSA and LST products were used to investigate the magnitude of differences in mean annual LSA and LST values between contrasting montado canopy cover percentages. As a result, it was found a significant statistical relationship between montado canopy cover percentage and mean annual surface albedo (R2 = 0.866, p < 0.001) and surface temperature (R2 = 0.942, p < 0.001). The comparisons between the four contrasting montado canopy cover classes showed marked differences in LSA (χ2 = 192.17, df = 3, p < 0.001) and LST (χ2 = 318.18, df = 3, p < 0.001). The highest montado canopy cover percentage (MT_4) generally had lower albedo than lowest canopy cover class, presenting a difference of −11.2% in mean annual albedo values. It was also showed that MT_4 and MT_3 are the cooler canopy cover classes, and MT_2 and MT_1 the warmer, where MT_1 class had a difference of 3.42 °C compared with MT_4 class. Overall, this research highlighted the role that potential changes in montado canopy cover may play in local land surface albedo and temperature variations, as an increase in these two biogeophysical parameters may potentially bring about, in the long term, local/regional climatic changes moving towards greater aridity.

Global runoff routing with the hydrological component of the ECMWF NWP system

A global river routing scheme coupled to the ECMWF land surface model is implemented and tested within the framework of the Global Soil Wetness Project II, to evaluate the feasibility of modelling global river runoff at a daily time scale. The exercise is designed to provide benchmark river runoff predictions needed to verify the land surface model. Ten years of daily runoff produced by the HTESSEL land surface scheme is input into the TRIP2 river routing scheme in order to generate daily river runoff. These are then compared to river runoff observations from the Global Runoff Data Centre (GRDC) in order to evaluate the potential and the limitations. A notable source of inaccuracy is bias between observed and modelled discharges which is not primarily due to the modelling system but instead of to the forcing and quality of observations and seems uncorrelated to the river catchment size. A global sensitivity analysis and Generalised Likelihood Uncertainty Estimation (GLUE) uncertainty analysis are applied to the global routing model. The ground water delay parameter is identified as being the most sensitive calibration parameter. Significant uncertainties are found in results, and those due to parameterisation of the routing model are quantified. The difficulty involved in parameterising global river discharge models is discussed. Detailed river runoff simulations are shown for the river Danube, which match well observed river runoff in upstream river transects. Results show that although there are errors in runoff predictions, model results are encouraging and certainly indicative of useful runoff predictions, particularly for the purpose of verifying the land surface scheme hydrologicly. Potential of this modelling system on future applications such as river runoff forecasting and climate impact studies is highlighted. Copyright © 2009 Royal Meteorological Society.

Detection of solar dimming and brightening effects on Northern Hemisphere river flow

Anthropogenic aerosols in the atmosphere have the potential to affect regional-scale land hydrology through solar dimming. Increased aerosol loading may have reduced historical surface evaporation over some locations, but the magnitude and extent of this effect is uncertain. Any reduction in evaporation due to historical solar dimming may have resulted in an increase in river flow. Here we formally detect and quantify the historical effect of changing aerosol concentrations, via solar radiation, on observed river flow over the heavily industrialized, northern extra-tropics. We use a state-of-the-art estimate of twentieth century surface meteorology as input data for a detailed land surface model, and show that the simulations capture the observed strong inter-annual variability in runoff in response to climatic fluctuations. Using statistical techniques, we identify a detectable aerosol signal in the observed river flow both over the combined region, and over individual river basins in Europe and North America. We estimate that solar dimming due to rising aerosol concentrations in the atmosphere around 1980 led to an increase in river runoff by up to 25% in the most heavily polluted regions in Europe. We propose that, conversely, these regions may experience reduced freshwater availability in the future, as air quality improvements are set to lower aerosol loading and solar dimming.

Exploiting satellite-based rainfall for weather index insurance: the challenges of spatial and temporal aggregation

Lack of access to insurance exacerbates the impact of climate variability on smallholder famers in Africa. Unlike traditional insurance, which compensates proven agricultural losses, weather index insurance (WII) pays out in the event that a weather index is breached. In principle, WII could be provided to farmers throughout Africa. There are two data-related hurdles to this. First, most farmers do not live close enough to a rain gauge with sufficiently long record of observations. Second, mismatches between weather indices and yield may expose farmers to uncompensated losses, and insurers to unfair payouts – a phenomenon known as basis risk. In essence, basis risk results from complexities in the progression from meteorological drought (rainfall deficit) to agricultural drought (low soil moisture). In this study, we use a land-surface model to describe the transition from meteorological to agricultural drought. We demonstrate that spatial and temporal aggregation of rainfall results in a clearer link with soil moisture, and hence a reduction in basis risk. We then use an advanced statistical method to show how optimal aggregation of satellite-based rainfall estimates can reduce basis risk, enabling remotely sensed data to be utilized robustly for WII.

The use of remotely sensed rainfall for managing drought risk: a case study of weather index insurance in Zambia

Remotely sensed rainfall is increasingly being used to manage climate-related risk in gauge sparse regions. Applications based on such data must make maximal use of the skill of the methodology in order to avoid doing harm by providing misleading information. This is especially challenging in regions, such as Africa, which lack gauge data for validation. In this study, we show how calibrated ensembles of equally likely rainfall can be used to infer uncertainty in remotely sensed rainfall estimates, and subsequently in assessment of drought. We illustrate the methodology through a case study of weather index insurance (WII) in Zambia. Unlike traditional insurance, which compensates proven agricultural losses, WII pays out in the event that a weather index is breached. As remotely sensed rainfall is used to extend WII schemes to large numbers of farmers, it is crucial to ensure that the indices being insured are skillful representations of local environmental conditions. In our study we drive a land surface model with rainfall ensembles, in order to demonstrate how aggregation of rainfall estimates in space and time results in a clearer link with soil moisture, and hence a truer representation of agricultural drought. Although our study focuses on agricultural insurance, the methodological principles for application design are widely applicable in Africa and elsewhere.

Performance Evaluation Of SMOS Soil Moisture Retrieval Parameters For Hydrological Application

Microwave remote sensing has high potential for soil moisture retrieval. However, the efficient retrieval of soil moisture depends on optimally choosing the soil moisture retrieval parameters. In this study first the initial evaluation of SMOS L2 product is performed and then four approaches regarding soil moisture retrieval from SMOS brightness temperature are reported. The radiative transfer equation based tau-omega rationale is used in this study for the soil moisture retrievals. The single channel algorithms (SCA) using H polarisation is implemented with modifications, which includes the effective temperatures simulated from ECMWF (downscaled using WRF-NOAH Land Surface Model (LSM)) and MODIS. The retrieved soil moisture is then utilized for soil moisture deficit (SMD) estimation using empirical relationships with Probability Distributed Model based SMD as a benchmark. The square of correlation during the calibration indicates a value of R2 =0.359 for approach 4 (WRF-NOAH LSM based LST with optimized roughness parameters) followed by the approach 2 (optimized roughness parameters and MODIS based LST) (R2 =0.293), approach 3 (WRF-NOAH LSM based LST with no optimization) (R2 =0.267) and approach 1(MODIS based LST with no optimization) (R2 =0.163). Similarly, during the validation a highest performance is reported by approach 4. The other approaches are also following a similar trend as calibration. All the performances are depicted through Taylor diagram which indicates that the H polarisation using ECMWF based LST is giving a better performance for SMD estimation than the original SMOS L2 products at a catchment scale.

Effects of RegCM3 parameterizations on simulated rainy season over South America

The impacts of change in the Grell convective scheme and biosphere-atmosphere transfer scheme (BATS) in RegCM3 are described. Three numerical experiments (RegZhang, RegClaris and RegArain) are conducted to reduce the RegCM3-Grell rainfall underestimation over tropical South America. The simulation referred to as RegZhang follows modifications made by Zhang et al. (2008) in the BATS. The RegClaris combines the RegZhang BATS parameters with a reduction of water drainage at the bottom of the subsoil layer in the regions covered by the tropical rain forest and a shorter convective time period for the Grell scheme. The RegArain considers this same modification in the Grell scheme, but uses a deeper total soil column and a deeper root system in the BATS. After the first year of simulation, the soil water content in RegZhang is progressively drained out of the soil column resulting in a deficit of rainfall in the Amazon. The RegClaris and RegArain, on the other hand, simulate a similar rainfall annual cycle in the Amazon, showing substantial improvement not only in phase but also in intensity. This improvement is partially related to an increase in evapotranspiration due to a larger availability of water in the soil column. A remote effect is also noted over the La Plata Basin region, where the larger summer rainfall rate may be related to the increase in moisture transport from the Amazon. Wind- and rainfall-based indices are applied to identify South American monsoon (SAM) timing. The RegClaris rainfall rates are adequate to identify the onset and the demise of SAM according to the observed data, whereas the rainfall deficit in RegZhang is associated with a delay in the onset and an early demise of the SAM.

Local flow under weak trade wind regime

[EN] On 8-10 April 2007, several episodes of intense sea-breeze fronts were registered at the islands of Fuerteventura and Lanzarote (Canary Islands). The sea-breeze circulation was primary driven by daytime heating contrasts between land and the Atlantic Ocean during a period of weak trade winds. Numerical simulations of these events were carried out using the 3.1.1 version of the Weather Research and Forecasting (WRF-ARW) Model. Three different domains with 6.6-km, 2.2-km and 0.7-km horizontal grid spacing and two sets with 51 and 70 vertical sigma levels were defined. The simulation was performed using two-way interactive nesting between the first and the second domain, using different land surface model parameterizations (Thermal diffusion, Noah LSM and RUC) for comparison. Initial conditions were provided by the NCAR Dataset analysis from April 2007, which were improved using surface and upper-air observations. The poster is focused on the 10 April episode.

Firsts results on the modelling of the Gran Canaria atmospheric wake

[EN] This poster shows the first attempt to modelize the Gran Canaria Island wake, an obstacle with almost a conical shape (60 km diameter and about 2000 m height). The leeside circulation was modelized for two well-defined street vortex cases during June 2010 and March 2011. Numerical simulations of these events were carried out using the 3.1.1 version of the Weather Research and Forecasting (WRF-ARW) Model. Three different domains with 4.5-km, 1.5-km and 0.5-km horizontal grid spacing and 70 vertical sigma levels were defined. The simulations were performed using two-way interactive nesting between the first and the second and third domains, using different land surface model parameterizations (Thermal diffusion, Noah LSM and RUC) for comparison. Initial conditions were provided by the NCAR Dataset analysis from April 2007. The poster is focused on both episodes using NoahLSM parameterizations.

Plant Functional Type (PFT) map over Siberia derived from GlobCover 2005 dataset, link to dataset in NetCDF format

High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover data sets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing Earth system models. Earth system models also require specific land cover classification systems based on plant functional types (PFTs), rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover data sets against one another and with auxiliary data to identify key uncertainties that contribute to variability in PFT classifications that would introduce errors in Earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to represent the water and carbon cycles in northern latitudes better. Updated land cover data sets are critical for improving and maintaining the relevance of Earth system models for assessing climate and human impacts on biogeochemistry and biophysics.

Analyse des données de la mission Gravity Recovery And Climate Experiment (GRACE) sur des bassins versants choisis au Canada pour la caractérisation des eaux souterraines.

Résumé : Les eaux souterraines ont un impact majeur sur la vie terrestre, les besoins domestiques et le climat. Elles sont aussi un maillon essentiel du cycle hydrologique. Au Canada par exemple, plus de 30 % de la population est tributaire des eaux souterraines pour leur alimentation en eau potable. Ces ressources subissent de nombreuses pressions sous l’influence de certains facteurs comme la salinisation, la contamination et l’épuisement. La variabilité du climat et la demande croissante sur ces ressources imposent l'amélioration de nos connaissances sur les eaux souterraines. L’objectif principal du projet de recherche est d’exploiter les données d’anomalies (TWS) de la mission Gravity Recovery And Climate Experiment (GRACE) pour localiser, quantifier et analyser les variations des eaux souterraines à travers les bassins versants du Bas-Mackenzie, du Saint-Laurent, du Nord-Québec et du Labrador. Il s’agit aussi d’analyser l’influence des cycles d’accumulation et de fonte de neige sur les variations du niveau des eaux souterraines. Pour estimer les variations des eaux souterraines, la connaissance des autres paramètres du bilan hydrologique est nécessaire. Ces paramètres sont estimés à l’aide des sorties du modèles de surface CLM du Système Global d’Assimilation des Données de la Terre (GLDAS). Les données GRACE qui ont été utilisées sont celles acquises durant la période allant de mars 2002 à août 2012. Les résultats ont été évalués à partir d’enregistrements de niveaux piézométriques provenant de 1841 puits localisés dans les aquifères libres du bassin des réseaux de suivi des eaux souterraines au Canada. Les valeurs de rendements spécifiques des différents types d’aquifères de chaque puits et celles des variations mensuelles du niveau d’eau dans ces puits ont été utilisées pour estimer les variations des anomalies des eaux souterraines in-situ. L’étude de corrélation entre les variations des anomalies des eaux souterraines estimées à partir de la combinaison GRACE-GLDAS et celles issues de données in-situ révèle des concordances significatives avec des valeurs de

3-D density structure and geological evolution of Stromboli volcano (Aeolian Islands, Italy) inferred from land-based and sea-surface gravity data

We present the first density model of Stromboli volcano (Aeolian Islands, Italy) obtained by simultaneously inverting land-based (543) and sea-surface (327) relative gravity data. Modern positioning technology, a 1 x 1 m digital elevation model, and a 15 x 15 m bathymetric model made it possible to obtain a detailed 3-D density model through an iteratively reweighted smoothness-constrained least-squares inversion that explained the land-based gravity data to 0.09 mGal and the sea-surface data to 5 mGal. Our inverse formulation avoids introducing any assumptions about density magnitudes. At 125 m depth from the land surface, the inferred mean density of the island is 2380 kg m(-3), with corresponding 2.5 and 97.5 percentiles of 2200 and 2530 kg m-3. This density range covers the rock densities of new and previously published samples of Paleostromboli I, Vancori, Neostromboli and San Bartolo lava flows. High-density anomalies in the central and southern part of the island can be related to two main degassing faults crossing the island (N41 and NM) that are interpreted as preferential regions of dyke intrusions. In addition, two low-density anomalies are found in the northeastern part and in the summit area of the island. These anomalies seem to be geographically related with past paroxysmal explosive phreato-magmatic events that have played important roles in the evolution of Stromboli Island by forming the Scari caldera and the Neostromboli crater, respectively. (C) 2014 Elsevier B.V. All rights reserved.

The simulation of the opposing fluxes of latent heat and CO2 over various land-use types: coupling a gas exchange model to a mesoscale atmospheric model

A mesoscale meteorological model (FOOT3DK) is coupled with a gas exchange model to simulate surface fluxes of CO2 and H2O under field conditions. The gas exchange model consists of a C3 single leaf photosynthesis sub-model and an extended big leaf (sun/shade) sub-model that divides the canopy into sunlit and shaded fractions. Simulated CO2 fluxes of the stand-alone version of the gas exchange model correspond well to eddy-covariance measurements at a test site in a rural area in the west of Germany. The coupled FOOT3DK/gas exchange model is validated for the diurnal cycle at singular grid points, and delivers realistic fluxes with respect to their order of magnitude and to the general daily course. Compared to the Jarvis-based big leaf scheme, simulations of latent heat fluxes with a photosynthesis-based scheme for stomatal conductance are more realistic. As expected, flux averages are strongly influenced by the underlying land cover. While the simulated net ecosystem exchange is highly correlated with leaf area index, this correlation is much weaker for the latent heat flux. Photosynthetic CO2 uptake is associated with transpirational water loss via the stomata, and the resulting opposing surface fluxes of CO2 and H2O are reproduced with the model approach. Over vegetated surfaces it is shown that the coupling of a photosynthesis-based gas exchange model with the land-surface scheme of a mesoscale model results in more realistic simulated latent heat fluxes.

Urban surface energy balance models: model characteristics and methodology for a comparison study

Many urban surface energy balance models now exist. These vary in complexity from simple schemes that represent the city as a concrete slab, to those which incorporate detailed representations of momentum and energy fluxes distributed within the atmospheric boundary layer. While many of these schemes have been evaluated against observations, with some models even compared with the same data sets, such evaluations have not been undertaken in a controlled manner to enable direct comparison. For other types of climate model, for instance the Project for Intercomparison of Land-Surface Parameterization Schemes (PILPS) experiments (Henderson-Sellers et al., 1993), such controlled comparisons have been shown to provide important insights into both the mechanics of the models and the physics of the real world. This paper describes the progress that has been made to date on a systematic and controlled comparison of urban surface schemes. The models to be considered, and their key attributes, are described, along with the methodology to be used for the evaluation.

The role of CO2 and dynamic vegetation on the impact of temperate land use change in the HadCM3 coupled climate model

Human induced land-use change (LUC) alters the biogeophysical characteristics of the land surface influencing the surface energy balance. The level of atmospheric CO2 is expected to increase in the coming century and beyond, modifying temperature and precipitation patterns and altering the distribution and physiology of natural vegetation. It is important to constrain how CO2-induced climate and vegetation change may influence the regional extent to which LUC alters climate. This sensitivity study uses the HadCM3 coupled climate model under a range of equilibrium forcings to show that the impact of LUC declines under increasing atmospheric CO2, specifically in temperate and boreal regions. A surface energy balance analysis is used to diagnose how these changes occur. In Northern Hemisphere winter this pattern is attributed in part to the decline in winter snow cover and in the summer due to a reduction in latent cooling with higher levels of CO2. The CO2-induced change in natural vegetation distribution is also shown to play a significant role. Simulations run at elevated CO2 yet present day vegetation show a significantly increased sensitivity to LUC, driven in part by an increase in latent cooling. This study shows that modelling the impact of LUC needs to accurately simulate CO2 driven changes in precipitation and snowfall, and incorporate accurate, dynamic vegetation distribution.