8 research outputs found
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Capability of the variogram to quantify the spatial patterns of surface fluxes and soil moisture simulated by land surface models
Up to now, relatively little effort has been dedicated to the quantitative assessment of the differences in spatial patterns of model outputs. In this paper, we employed a variogram-based methodology to quantify the differences in the spatial patterns of root-zone soil moisture, net radiation, and latent and sensible heat fluxes simulated by three land surface models (SURFEX/ISBA, JULES and CHTESSEL) over three European geo- graphic domains â namely, UK, France and Spain. The model output spatial patterns were quantified through two metrics derived from the variogram: i) the variogram sill, which quantifies the degree of spatial variability of the data; and ii) the variogram integral range, which represents the spatial length scale of the data. The higher seasonal variation of the spatial variability of sensible and latent heat fluxes over France and Spain, compared to the UK, is related to a more frequent occurrence of a soil-moisture-limited evapotranspiration regime during summer dry spells in the south of France and Spain. The small differences in spatial variability of net radiation between models indicate that the spatial patterns of net radiation are mostly driven by the climate forcing data set. However, the models exhibit larger differences in latent and sensible heat flux spatial variabilities, which are related to their differences in i) soil and vegetation ancillary datasets and ii) physical process representation. The highest discrepancies in spatial patterns between models are observed for soil moisture, which is mainly related to the type of soil hydraulic function implemented in the models. This work demonstrates the capability of the variogram to enhance our understanding of the spatiotemporal structure of the uncertainties in land surface model outputs. Therefore, we strongly encourage the implementation of the variogram metrics in model intercomparison exercises
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The effect of land surface hydrological process representation on drought prediction, at a range of spatio-temporal scales
Droughts can have a devastating impact on livelihoods. In order to predict and understand drought processes in the earth system, land surface models along with agro/hydrological models are currently used. This study focuses on improving our understanding of how best to model agricultural and soil moisture drought. The specific aim is to assess the sensitivity and configurations of agrohydrological (the SWAP model) and land surface models (JULES and CH/CTESSEL) regarding their drought prediction performance over the UK and Europe.
In Chapter 4 the sensitivity of the SWAP agrohydrological model and CHTESSEL land surface model drought-related variables to soil hydraulic parameters (van Genuchten) were analysed by the Sobol' method. The most important soil hydraulic parameters in the SWAP model depended on the soil texture and soil moisture conditions. The model was found to be most sensitive to van Genuchten a and
saturated water content (0s ).
In Chapter 5, it was found that the modelled soil moisture of CHTESSEL was most sensitive to the saturated hydraulic conductivity (Ks ) and the saturated water
content (0s )- The uncertainty ranges of Ks and 0s were used to produce a drought probability map of Europe for August 2003.
In Chapter 6, the performance of the SWAP model under drought conditions for seven crop types was compared. The comparison was based on the modelled agricultural drought indices of Soil Moisture Deficit Index (SMDI), where SWAP was run with dynamic rooting depth, and Evapotranspiration Deficit Index (ETDI). The results showed that the SWAP model simulation of crops' response to drought was sensitive to the length of crop roots, transpiration reduction coefficient, LAI and soil
type.
In Chapter 7, CHTESSEL, CTESSEL, JULES with Brooks and Corey hydraulic configuration (JULESBC) and JULES with van Genuchten hydraulic configuration (JULESVG) were compared regarding three types of key approaches that affects model performance in prediction of drought: 1) Stomata/ conductance: The A-gs approach (in CTESSEL) resulted in more water use efficiency within the model in
comparison to the Jarvis approach (in CHTESSEL); 2) Hydraulic parameterisation: The Brooks and Corey scheme (in JULESBC) resulted in longer and more intense
droughts compared to the van Genuchten scheme (in JULESVG); 3) implementation of soil water availability and model type: The difference in performance between
CTESSEL and JULESVG was greater than comparisons between the stomata! conductance or hydraulic schemes for each separate model. This work has demonstrated that there are clear parameter sensitivities in both
agrohydrological and land surface models, and the values assigned to the soil hydraulic and plant parameters. The sensitivities should be considered carefully
when using these models for drought forecasting and prediction. In addition the choice of stomata! conductance approach and soil hydraulic algorithm has a large effect on drought prediction. This effect is even greater when comparing between different land surface models. Overall this work has demonstrated the importance of undertaking parameter, algorithm and model choice sensitivity studies in order to appreciate the uncertainties in drought prediction and be confident in forecasting future droughts
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Assessment of short-range forecast error atmosphere-ocean cross-correlations from the Met Office coupled NWP system
Operational data assimilation systems for coupled atmosphere -ocean prediction are usually âweakly-coupledâ, in which there is no explicit interaction between the atmosphere and ocean within the data assimilation step. Explicitly allowing for cross-correlations between the ocean and the atmosphere may have potential benefits in improving the consistency of atmosphere and ocean analyses, as well as allowing a better use of observations at the interface. To understand whether such correlations are significant on the timescales of numerical weather prediction, we investigate the atmosphere-ocean cross-correlations of short term forecast errors from the Met Office coupled prediction system, considering their temporal and spatial variability. We find that significant correlations exist between atmosphere and ocean forecast errors on these timescales, and that these vary diurnally, from day to day, spatially and synoptically. For correlations between errors in the atmospheric wind and ocean temperature, positive correlations in the North Atlantic region are found to be synoptically dependent, with correlation structures extending into the ocean throughout the deep mixed layer, beyond a depth of 100m. In contrast, negative correlations over the Indian Ocean are very shallow and are associated with the diurnal cycle of solar radiation. The significance and variability of cross-correlations indicates that there should be a benefit from including them in data assimilation systems, but it will be important to allow for some flow-dependence in the correlations. Furthermore, the differing vertical extents of the cross-correlations in different regions implies the need for situation-dependent localization of ensemble correlations when including them in coupled data assimilation systems
Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements
Abstract. Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the âsoil14_psiâ experiments), when the critical threshold value for inducing soil moisture stress was reduced (âsoil14_p0â), and when plants were able to access soil moisture in deeper soil layers (âsoil14_dr*2â). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased model biases but improved the simulated seasonal cycle and brought the monthly variance closer to the measured variance of LE. Further evaluation of the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES or as a general way to improve land surface carbon and water fluxes in other models. In addition, using soil matric potential presents the opportunity to include plant functional type-specific parameters to further improve modeled fluxes
Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements
International audienceDrought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the "soill4_psi" experiments), when the critical threshold value for inducing soil moisture stress was reduced ("soil14_p0"), and when plants were able to access soil moisture in deeper soil layers ("soil14_dr*2"). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased model biases but improved the simulated seasonal cycle and brought the monthly variance closer to the measured variance of LE. Further evaluation of the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES or as a general way to improve land surface carbon and water fluxes in other models. In addition, using soil matric potential presents the opportunity to include plant functional type-specific parameters to further improve modeled fluxes