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  • 1
    Publication Date: 2018-02-01
    Description: This study compares four model systems in three configurations (LSM, LSM + GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly underrepresent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land–atmosphere coupling) and may overrepresent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally underrepresent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly, and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Although the models validate better against Bowen-ratio-corrected surface flux observations, which allow for closure of surface energy balances at flux tower sites, it is not clear whether the corrected fluxes are more representative of actual fluxes. The analysis illuminates targets for coupled land–atmosphere model development, as well as the value of long-term globally distributed observational monitoring.
    Print ISSN: 1525-755X
    Electronic ISSN: 1525-7541
    Topics: Geography , Geosciences , Physics
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  • 2
    Publication Date: 2016-11-01
    Description: In the last decade operational probabilistic ensemble flood forecasts have become common in supporting decision-making processes leading to risk reduction. Ensemble forecasts can assess uncertainty, but they are limited to the uncertainty in a specific modeling system. Many of the current operational flood prediction systems use a multimodel approach to better represent the uncertainty arising from insufficient model structure. This study presents a multimodel approach to building a global flood prediction system using multiple atmospheric reanalysis datasets for river initial conditions and multiple TIGGE forcing inputs to the ECMWF land surface model. A sensitivity study is carried out to clarify the effect of using archive ensemble meteorological predictions and uncoupled land surface models. The probabilistic discharge forecasts derived from the different atmospheric models are compared with those from the multimodel combination. The potential for further improving forecast skill by bias correction and Bayesian model averaging is examined. The results show that the impact of the different TIGGE input variables in the HTESSEL/Catchment-Based Macroscale Floodplain model (CaMa-Flood) setup is rather limited other than for precipitation. This provides a sufficient basis for evaluation of the multimodel discharge predictions. The results also highlight that the three applied reanalysis datasets have different error characteristics that allow for large potential gains with a multimodel combination. It is shown that large improvements to the forecast performance for all models can be achieved through appropriate statistical postprocessing (bias and spread correction). A simple multimodel combination generally improves the forecasts, while a more advanced combination using Bayesian model averaging provides further benefits.
    Print ISSN: 1525-755X
    Electronic ISSN: 1525-7541
    Topics: Geography , Geosciences , Physics
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  • 3
    Publication Date: 2016-03-15
    Description: Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those it is found that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely because of differences in instrumentation, calibration, and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat-dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory), and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but they poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration, or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison.
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    Electronic ISSN: 1525-7541
    Topics: Geography , Geosciences , Physics
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  • 4
    Publication Date: 2016-05-25
    Description: 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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    Electronic ISSN: 1525-7541
    Topics: Geography , Geosciences , Physics
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  • 5
    Publication Date: 2016-09-26
    Description: Reanalysis products represent a valuable source of information for different impact modelling and monitoring activities over regions with sparse observational data. It is therefore essential to evaluate their behavior and their intrinsic uncertainties. In this study, we focus on precipitation over monsoon Asia, a key agricultural region of the world. Four reanalysis datasets are evaluated, namely ERA-Interim, ERA-Interim/Land, AgMerra and JRA-55. APHRODITE and CHIRPS are the two gridded observational datasets used for the evaluation; the latter is based on rain gauge data, while the former on satellite/rain gauge data. Differences in seasonality, moderate-to-heavy precipitation events, daily distribution and drought characteristics are analyzed. Results show remarkable differences between the APHRODITE and CHIRPS observational datasets as well as between these datasets and the reanalyses. AgMerra generally achieves the best performance, but it is not a near real time updated dataset. ERA-ILand shows good spatial performance; but when the interest is on the temporal evolution, JRA-55 is recommended, as it exhibits the most stable temporal behavior. This study shows that the use of reanalyses for impact modelling and monitoring over monsoon Asia requires an accurate evaluation and choices to be tailored to the specific needs.
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
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  • 6
    Publication Date: 2019-07-30
    Description: We apply spectral empirical orthogonal function (SEOF) analysis to educe climate patterns as dominant spatiotemporal modes of variability from reanalysis data. SEOF is a frequency-domain variant of standard empirical orthogonal function (EOF) analysis, and computes modes that represent the statistically most relevant and persistent patterns from an eigendecomposition of the estimated cross-spectral density matrix (CSD). The spectral estimation step distinguishes the approach from other frequency-domain EOF methods based on a single realization of the Fourier transform, and results in a number of desirable mathematical properties: at each frequency, SEOF yields a set of orthogonal modes that are optimally ranked in terms of variance in the L2 sense, and that are coherent in both space and time by construction. We discuss the differences between SEOF and other competing approaches, as well as its relation to dynamical modes of stochastically forced, nonnormal linear dynamical systems. The method is applied to ERA-Interim and ERA-20C reanalysis data, demonstrating its ability to identify a number of well-known spatiotemporal coherent meteorological patterns and teleconnections, including the Madden–Julian oscillation (MJO), the quasi-biennial oscillation (QBO), and the El Niño–Southern Oscillation (ENSO) (i.e., a range of phenomena reoccurring with average periods ranging from months to many years). In addition to two-dimensional univariate analyses of surface data, we give examples of multivariate and three-dimensional meteorological patterns that illustrate how this technique can systematically identify coherent structures from different sets of data. The MATLAB code used to compute the results presented in this study, including the download scripts for the reanalysis data, is freely available online.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
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  • 7
    Publication Date: 2010-02-01
    Description: 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.
    Print ISSN: 0882-8156
    Electronic ISSN: 1520-0434
    Topics: Geography , Physics
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  • 8
    Publication Date: 2012-04-01
    Description: Three different complexity snow schemes implemented in the ECMWF land surface scheme Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) are evaluated within the EC-EARTH climate model. The snow schemes are (i) the original HTESSEL single-bulk-layer snow scheme, (ii) a new snow scheme in operations at ECMWF since September 2009, and (iii) a multilayer version of the previous. In offline site simulations, the multilayer scheme outperforms the single-layer schemes in deep snowpack conditions through its ability to simulate sporadic melting events thanks to the lower thermal inertial of the uppermost layer. Coupled atmosphere–land/snow simulations performed by the EC-EARTH climate model are validated against remote sensed snow cover and surface albedo. The original snow scheme has a systematic early melting linked to an underestimation of surface albedo during spring that was partially reduced with the new snow schemes. A key process to improve the realism of the near-surface atmospheric temperature and at the same time the soil freezing is the thermal insulation of the snowpack (tightly coupled with the accuracy of snow mass and density simulations). The multilayer snow scheme outperforms the single-layer schemes in open deep snowpack (such as prairies or tundra in northern latitudes) and is instead comparable in shallow snowpack conditions. However, the representation of orography in current climate models implies limitations for accurately simulating the snowpack, particularly over complex terrain regions such as the Rockies and the Himalayas.
    Print ISSN: 1525-755X
    Electronic ISSN: 1525-7541
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  • 9
    Publication Date: 2011-10-01
    Description: The second phase of the Global Land–Atmosphere Coupling Experiment (GLACE-2) is a multi-institutional numerical modeling experiment focused on quantifying, for boreal summer, the subseasonal (out to two months) forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture. An overview of the experiment and model behavior at the global scale is described here, along with a determination and characterization of multimodel “consensus” skill. The models show modest but significant skill in predicting air temperatures, especially where the rain gauge network is dense. Given that precipitation is the chief driver of soil moisture, and thereby assuming that rain gauge density is a reasonable proxy for the adequacy of the observational network contributing to soil moisture initialization, this result indeed highlights the potential contribution of enhanced observations to prediction. Land-derived precipitation forecast skill is much weaker than that for air temperature. The skill for predicting air temperature, and to some extent precipitation, increases with the magnitude of the initial soil moisture anomaly. GLACE-2 results are examined further to provide insight into the asymmetric impacts of wet and dry soil moisture initialization on skill.
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  • 10
    Publication Date: 2009-06-01
    Description: The Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) is used operationally in the Integrated Forecast System (IFS) for describing the evolution of soil, vegetation, and snow over the continents at diverse spatial resolutions. A revised land surface hydrology (H-TESSEL) is introduced in the ECMWF operational model to address shortcomings of the land surface scheme, specifically the lack of surface runoff and the choice of a global uniform soil texture. New infiltration and runoff schemes are introduced with a dependency on the soil texture and standard deviation of orography. A set of experiments in stand-alone mode is used to assess the improved prediction of soil moisture at the local scale against field site observations. Comparison with basin-scale water balance (BSWB) and Global Runoff Data Centre (GRDC) datasets indicates a consistently larger dynamical range of land water mass over large continental areas and an improved prediction of river runoff, while the effect on atmospheric fluxes is fairly small. Finally, the ECMWF data assimilation and prediction systems are used to verify the effect on surface and near-surface quantities in the atmospheric-coupled mode. A midlatitude error reduction is seen both in soil moisture and in 2-m temperature.
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    Topics: Geography , Geosciences , Physics
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