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1. Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction Project, Phase i (LS4P-I): Organization and experimental design

Author

Xue, Y, Yao, T, Boone, AA, Diallo, I, Liu, Y, Zeng, X, Lau, WKM, Sugimoto, S, Tang, Q, Pan, X, Van Oevelen, PJ, Klocke, D, Koo, MS, Sato, T, Lin, Z, Takaya, Y, Ardilouze, C, Materia, S, Saha, SK, Senan, R, Nakamura, T, Wang, H, Yang, J, Zhang, H, Zhao, M, Liang, XZ, Neelin, JD, Vitart, F, Li, X, Zhao, P, Shi, C, Guo, W, Tang, J, Yu, M, Qian, Y, Shen, SSP, Zhang, Y, Yang, K, Leung, R, Qiu, Y, Peano, D, Qi, X, Zhan, Y, Brunke, MA, Chou, SC, Ek, M, Fan, T, Guan, H, Lin, H, Liang, S, Wei, H, Xie, S, Xu, H, Li, W, Shi, X, Nobre, P, Pan, Y, Qin, Y, Dozier, J, Ferguson, CR, Balsamo, G, Bao, Q, Feng, J, Hong, J, Hong, S, Huang, H, Ji, D, Ji, Z, Kang, S, Lin, Y, Liu, W, Muncaster, R, De Rosnay, P, Takahashi, HG, Wang, G, Wang, S, Wang, W, Zhou, X, and Zhu, Y

Subjects
Earth Sciences
Abstract

Subseasonal-to-seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging, but also has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiative called "Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction"(LS4P) as the first international grass-roots effort to introduce spring land surface temperature (LST)/subsurface temperature (SUBT) anomalies over high mountain areas as a crucial factor that can lead to significant improvement in precipitation prediction through the remote effects of land-atmosphere interactions. LS4P focuses on process understanding and predictability, and hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than 40 groups worldwide have participated in this effort, including 21 Earth system models, 9 regional climate models, and 7 data groups. This paper provides an overview of the history and objectives of LS4P, provides the first-phase experimental protocol (LS4P-I) which focuses on the remote effect of the Tibetan Plateau, discusses the LST/SUBT initialization, and presents the preliminary results. Multi-model ensemble experiments and analyses of observational data have revealed that the hydroclimatic effect of the spring LST on the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation beyond East Asia and its S2S prediction. Preliminary studies and analysis have also shown that LS4P models are unable to preserve the initialized LST anomalies in producing the observed anomalies largely for two main reasons: (i) inadequacies in the land models arising from total soil depths which are too shallow and the use of simplified parameterizations, which both tend to limit the soil memory; (ii) reanalysis data, which are used for initial conditions, have large discrepancies from the observed mean state and anomalies of LST over the Tibetan Plateau. Innovative approaches have been developed to largely overcome these problems.

Published
2021

2. Infiltration from the pedon to global grid scales: An overview and outlook for land surface modeling

Author

Vereecken, H, Weihermüller, L, Assouline, S, Šimůnek, J, Verhoef, A, Herbst, M, Archer, N, Mohanty, B, Montzka, C, Vanderborght, J, Balsamo, G, Bechtold, M, Boone, A, Chadburn, S, Cuntz, M, Decharme, B, Ducharne, A, Ek, M, Garrigues, S, Goergen, K, Ingwersen, J, Kollet, S, Lawrence, DM, Li, Q, Or, D, Swenson, S, de Vrese, P, Walko, R, Wu, Y, and Xue, Y

Subjects
Physical Geography and Environmental Geoscience, Soil Sciences, Crop and Pasture Production, Environmental Engineering
Abstract

Infiltration in soils is a key process that partitions precipitation at the land surface into surface runoff and water that enters the soil profile. We reviewed the basic principles of water infiltration in soils and we analyzed approaches commonly used in land surface models (LSMs) to quantify infiltration as well as its numerical implementation and sensitivity to model parameters. We reviewed methods to upscale infiltration from the point to the field, hillslope, and grid cell scales of LSMs. Despite the progress that has been made, upscaling of local-scale infiltration processes to the grid scale used in LSMs is still far from being treated rigorously. We still lack a consistent theoretical framework to predict effective fluxes and parameters that control infiltration in LSMs. Our analysis shows that there is a large variety of approaches used to estimate soil hydraulic properties. Novel, highly resolved soil information at higher resolutions than the grid scale of LSMs may help in better quantifying subgrid variability of key infiltration parameters. Currently, only a few LSMs consider the impact of soil structure on soil hydraulic properties. Finally, we identified several processes not yet considered in LSMs that are known to strongly influence infiltration. Especially, the impact of soil structure on infiltration requires further research. To tackle these challenges and integrate current knowledge on soil processes affecting infiltration processes into LSMs, we advocate a stronger exchange and scientific interaction between the soil and the land surface modeling communities.

Published
2019

3. From Bottom-Up to Top-Down : Europe’s CO 2 Emissions Monitoring Capacity

Author

Janssens-Maenhout, G., Pinty, B., Dowell, M., Zunker, H., Andersson, E., Balsamo, G., Bézy, J.-L., Brunhes, T., Bösch, H., Bojkov, B., Brunner, D., Buchwitz, M., Crisp, D., Ciais, P., Counet, P., Dee, D., van der Gon, H. Denier, Dolman, H., Drinkwater, M. R., Dubovik, O., Engelen, R., Fehr, T., Fernandez, V., Heimann, M., Holmlund, K., Houweling, S., Husband, R., Juvyns, O., Kentarchos, A., Landgraf, J., Lang, R., Löscher, A., Marshall, J., Meijer, Y., Nakajima, M., Palmer, P. I., Peylin, P., Rayner, P., Scholze, M., Sierk, B., Tamminen, J., and Veefkind, P.

Published
2021

4. Toward an Operational Anthropogenic CO₂ Emissions Monitoring and Verification Support Capacity

Author

Janssens-Maenhout, G., Pinty, B., Dowell, M., Zunker, H., Andersson, E., Balsamo, G., Bézy, J.-L., Brunhes, T., Bösch, H., Bojkov, B., Brunner, D., Buchwitz, M., Crisp, D., Ciais, P., Counet, P., Dee, D., van der Gon, H. Denier, Dolman, H., Drinkwater, M. R., Dubovik, O., Engelen, R., Fehr, T., Fernandez, V., Heimann, M., Holmlund, K., Houweling, S., Husband, R., Juvyns, O., Kentarchos, A., Landgraf, J., Lang, R., Löscher, A., Marshall, J., Meijer, Y., Nakajima, M., Palmer, P. I., Peylin, P., Rayner, P., Scholze, M., Sierk, B., Tamminen, J., and Veefkind, P.

Published
2020

5. From Bottom-Up to Top-Down: Europe's C[O.sub.2] Emissions Monitoring Capacity

Author

Janssens-Maenhout, G., Pinty, B., Dowell, M., Zunker, H., Andersson, E., Balsamo, G., Bezy, J.-L., Brunhes, T., Bosch, H., Bojkov, B., Brunner, D., Buchwitz, M., Crisp, D., Ciais, P., Counet, P., Dee, D., van der Gon, H. Denier, Dolman, H., Drinkwater, M.R., Dubovik, O., Engelen, R., Fehr, T., Fernandez, V., Heimann, M., Holmlund, K., Houweling, S., Husband, R., Juvyns, O., Kentarchos, A., Landgraf, J., Lang, R., Loscher, A., Marshall, J., Meijer, Y., Nakajima, M., Palmer, P.I., Peylin, P., Rayner, P., Scholze, M., Sierk, B., Tamminen, J., and Veefkind, P.

Subjects
Greenhouse gases -- Measurement, Emissions (Pollution) -- Measurement, Air pollution -- Measurement, Business, Earth sciences, United Nations Framework Convention on Climate Change, 2015
Abstract

The Paris Agreement of 2015 established an enhanced transparency framework, in which parties of the agreement compile and provide their national greenhouse gas (GHG) inventories based on annual statistics of [...]

Published
2021
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6. Water balance in the amazon basin from a land surface model ensemble

Author

Getirana, ACV, Dutra, E, Guimberteau, M, Kam, J, Li, HY, Decharme, B, Zhang, Z, Ducharne, A, Boone, A, Balsamo, G, Rodell, M, Toure, AM, Xue, Y, Peters-Lidard, CD, Kumar, SV, Arsenault, K, Drapeau, G, Leung, LR, Ronchail, J, and Sheffield, J

Subjects
Amazon region, Runoff, Hydrologic models, Land surface model, Meteorology & Atmospheric Sciences, Atmospheric Sciences
Abstract

Despite recent advances in land surfacemodeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-ofthe- art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 18 spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to matchmonthly Global Precipitation Climatology Project (GPCP) andGlobal Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l'Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets andGravity Recovery and ClimateExperiment (GRACE)TWSestimates in two subcatchments of main tributaries (Madeira and Negro Rivers).At the basin scale, simulated ET ranges from 2.39 to 3.26mmday-1 and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.

Published
2014

7. ECMWF and UK Met Office Offshore Blowing Winds: Impact of Horizontal Resolution and Coastal Orography.

Author

Cavaleri, L., Balsamo, G., Beljaars, A., Bertotti, L., Davison, S., Edwards, J., Kanehama, T., and Wedi, N.

Subjects
TURBULENT diffusion (Meteorology), COASTS, NUMERICAL weather forecasting, ATMOSPHERIC models, GRAVITY waves, OFFSHORE wind power plants, WIND speed
Abstract

We analyze the performance of the European Centre for Medium‐Range Weather Forecasts (ECMWF) and UK Met Office (UKMO) meteorological models in predicting offshore blowing wind in coastal areas. Our attention is mainly on the Mediterranean coast, up to 200 km distance from the shore. We compare forecast neutral winds with Advanced Scatterometer measurements. The results indicate that the ECMWF forecasts systematically underestimate wind speed with respect to scatterometer data, while the UKMO model tends to overestimate. A cross‐analysis suggests that, better than fetch, model biases are a function of the model horizontal discretization, hence of the number of grid steps the wind runs over the sea. The steepness and roughness of the land orography before entering the sea, together with the related drag parameters, appear to have a strong role in determining the coastal offshore wind values. Surface drag over land tends to reduce the related wind speed, and it takes a few grid points to adjust to the smooth sea surface conditions. This is supported by a detailed study with a high resolution grid, but different orography resolutions. In these simulations, practically identical coastal wind speed distributions are found when the subgrid orography schemes are switched off. Vertical cross‐sections of potential temperature and wind in bora and mistral conditions illustrate the strong role of gravity waves, wind channeling and turbulent diffusion in coastal numerical weather prediction. Besides local wave modeling, this may be relevant for offshore wind farms, typically situated within 5–50 km from the coast. Plain Language Summary: Practical experience shows that European Centre for Medium‐Range Weather Forecasts coastal wind speeds, particularly when blowing from land to sea, are often underestimated. We have quantified this error by comparing model data with satellite measured ones. The results indicate that surface wind speeds progressively increase the further we go from the coast, reaching the correct values at about 200 km offshore. Analysis of the inner land distribution and related orographic characteristics strongly suggests that the underestimate is associated to the roughness of the surface the winds pass across before reaching the coast. This roughness is strongly dependent on the model resolution. The passage of the air flow over steep orography before reaching the coast leads to gravity waves, that is, vertical oscillations of the main flow, that strongly affect the distribution of the surface coastal winds and the time, and space, required to reach the correct values. Key Points: Meteorological model often underestimates offshore blowing wind speeds. We quantify and explain the reasons for itRather than distance, the number of grid steps off the coast, about 10, is crucial in reaching offshore the correct wind speedsCoastal orography is crucial in determining the underestimate at the coast, with also gravity waves determining the fetch evolution [ABSTRACT FROM AUTHOR]

Published
2024
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10. The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2019

Author

Petrescu, A.M.R., Qiu, C., McGrath, M.J., Peylin, P., Peters, G.P., Ciais, P., Thompson, R.L., Tsuruta, A., Brunner, D., Kuhnert, M., Matthews, B., Palmer, P.I., Tarasova, O., Regnier, P., Lauerwald, R., Bastviken, D., Höglund-Isaksson, L., Winiwarter, W., Etiope, G., Aalto, T., Balsamo, G., Bastrikov, V., Berchet, A., Brockmann, P., Ciotoli, G., Conchedda, G., Crippa, M., Dentener, F., Groot Zwaaftink, C.D., Guizzardi, D., Günther, D., Haussaire, J.-M., Houweling, S., Janssens-Maenhout, G., Kouyate, M., Leip, A., Leppänen, A., Lugato, E., Maisonnier, M., Manning, A.J., Markkanen, T., McNorton, J., Muntean, M., Oreggioni, G.D., Patra, P.K., Perugini, L., Pison, I., Raivonen, M.T., Saunois, M., Segers, A.J., Smith, P., Solazzo, E., Tian, H., Tubiello, F.N., Vesala, T., van der Werf, G.R., Wilson, C., Zaehle, S., Petrescu, A.M.R., Qiu, C., McGrath, M.J., Peylin, P., Peters, G.P., Ciais, P., Thompson, R.L., Tsuruta, A., Brunner, D., Kuhnert, M., Matthews, B., Palmer, P.I., Tarasova, O., Regnier, P., Lauerwald, R., Bastviken, D., Höglund-Isaksson, L., Winiwarter, W., Etiope, G., Aalto, T., Balsamo, G., Bastrikov, V., Berchet, A., Brockmann, P., Ciotoli, G., Conchedda, G., Crippa, M., Dentener, F., Groot Zwaaftink, C.D., Guizzardi, D., Günther, D., Haussaire, J.-M., Houweling, S., Janssens-Maenhout, G., Kouyate, M., Leip, A., Leppänen, A., Lugato, E., Maisonnier, M., Manning, A.J., Markkanen, T., McNorton, J., Muntean, M., Oreggioni, G.D., Patra, P.K., Perugini, L., Pison, I., Raivonen, M.T., Saunois, M., Segers, A.J., Smith, P., Solazzo, E., Tian, H., Tubiello, F.N., Vesala, T., van der Werf, G.R., Wilson, C., and Zaehle, S.

Abstract

Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27 + UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990–2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH4 yr−1 (EDGARv6.0, last year 2018) and 18.4 Tg CH4 yr−1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1 Tg CH4 yr−1. TD inversion estim

Published
2023
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11. The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom: 1990–2020

Author

McGrath, M.J., Petrescu, A.M.R., Peylin, P., Andrew, R.M., Matthews, B., Dentener, F., Balkovič, J., Bastrikov, V., Becker, M., Broquet, G., Ciais, P., Fortems-Cheiney, A., Ganzenmüller, R., Grassi, G., Harris, I., Jones, M., Knauer, J., Kuhnert, M., Monteil, G., Munassar, S., Palmer, P.I., Peters, G.P., Qiu, C., Schelhaas, M.-J., Tarasova, O., Vizzarri, M., Winkler, K., Balsamo, G., Berchet, A., Briggs, P., Brockmann, P., Chevallier, F., Conchedda, G., Crippa, M., Dellaert, S.N.C., Denier van der Gon, H.A.C., Filipek, S., Friedlingstein, P., Fuchs, R., Gauss, M., Gerbig, C., Guizzardi, D., Günther, D., Houghton, R.A., Janssens-Maenhout, G., Lauerwald, R., Lerink, B., Luijkx, I.T., Moulas, G., Muntean, M., Nabuurs, G.-J., Paquirissamy, A., Perugini, L., Peters, W., Pilli, R., Pongratz, J., Regnier, P., Scholze, M., Serengil, Y., Smith, P., Solazzo, E., Thompson, R.L., Tubiello, F.N., Vesala, T., Walther, S., McGrath, M.J., Petrescu, A.M.R., Peylin, P., Andrew, R.M., Matthews, B., Dentener, F., Balkovič, J., Bastrikov, V., Becker, M., Broquet, G., Ciais, P., Fortems-Cheiney, A., Ganzenmüller, R., Grassi, G., Harris, I., Jones, M., Knauer, J., Kuhnert, M., Monteil, G., Munassar, S., Palmer, P.I., Peters, G.P., Qiu, C., Schelhaas, M.-J., Tarasova, O., Vizzarri, M., Winkler, K., Balsamo, G., Berchet, A., Briggs, P., Brockmann, P., Chevallier, F., Conchedda, G., Crippa, M., Dellaert, S.N.C., Denier van der Gon, H.A.C., Filipek, S., Friedlingstein, P., Fuchs, R., Gauss, M., Gerbig, C., Guizzardi, D., Günther, D., Houghton, R.A., Janssens-Maenhout, G., Lauerwald, R., Lerink, B., Luijkx, I.T., Moulas, G., Muntean, M., Nabuurs, G.-J., Paquirissamy, A., Perugini, L., Peters, W., Pilli, R., Pongratz, J., Regnier, P., Scholze, M., Serengil, Y., Smith, P., Solazzo, E., Thompson, R.L., Tubiello, F.N., Vesala, T., and Walther, S.

Abstract

Quantification of land surface–atmosphere fluxes of carbon dioxide (CO2) and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural (including formally managed ecosystems) sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed and unmanaged ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven categorical model results, and inverse modeling estimates, extending the previous period 1990–2018 to the year 2020 to the extent possible. BU and TD products are compared with the European national greenhouse gas inventory (NGHGI) reported by parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU member states following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing the NGHGI with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categorie

Published
2023
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12. Benchmarking land initial conditions for high-resolution coupled forecasts of climate extremes

Author

Denissen, J., Arduini, G., Suetzl, B., Pedruzo Bagazgoitia, X., Boussetta, S., Choulga, M., Sandu, I., Zsoter, E., Mazzetti, C., Harrigan, S., Prudhomme, C., and Balsamo, G.

Abstract

Climate extremes, such as extreme precipitation, heat waves and droughts, can have severe implications for human health and ecosystems. Forecasting such events with higher skill can help to mitigate impacts. Increasing computational efficiency and an ever-increasing volume of satellite observations make it possible to forecast climate extremes at higher spatiotemporal resolutions, thereby better resolving small-scale variability of, amongst others, orography and land cover.Within the Destination Earth programme, coupled forecasts are executed globally down to the km-scale. A challenge for new high-resolution simulation set-ups is to provide initial conditions to the model. For the first set of runs, land initial conditions were interpolated from a 9km grid resolution, which are prone to errors in mountainous areas and regions with highly heterogeneous land cover. By running ECMWF’s land surface modelling component (ECLand) with meteorological forcing from both the operational forecast and the reanalysis model, resolution-specific land initial conditions are generated that make use of high-resolution ancillary fields, such as orography and land cover. From these land initial conditions, the skin temperature, soil moisture and temperature, snow cover and river discharge are benchmarked against observations, thereby validating key quantities in the surface energy and water balance. Then, the coupled forecasts can be run initialised from the best land initial conditions. We study the differences between coupled forecasts initialised with i) operationally interpolated or resolution-specific ii) operationally forced or iii) reanalysis-forced land initial conditions to evaluate whether using the newly attained resolution-specific land initial conditions affects the skill to forecast climate extremes., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Published
2023
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13. The Plumbing of Land Surface Models : Benchmarking Model Performance

Author

Best, M. J., Abramowitz, G., Johnson, H. R., Pitman, A. J., Balsamo, G., Boone, A., Cuntz, M., Decharme, B., Dirmeyer, P. A., Dong, J., Ek, M., Guo, Z., Haverd, V., van den Hurk, B. J. J., Nearing, G. S., Pak, B., Peters-Lidard, C., Santanello, J. A., Stevens, L., and Vuichard, N.

Published
2015

17. Spring Land Temperature in Tibetan Plateau and Global-Scale Summer Precipitation

Author

Xue, Y, Xue, Y, Diallo, I, Boone, AA, Yao, T, Zhang, Y, Zeng, X, Neelin, JD, Lau, WKM, Pan, Y, Liu, Y, Pan, X, Tang, Q, Van Oevelen, PJ, Sato, T, Koo, MS, Materia, S, Shi, C, Yang, J, Ardilouze, C, Lin, Z, Qi, X, Nakamura, T, Saha, SK, Senan, R, Takaya, Y, Wang, H, Zhang, H, Zhao, M, Nayak, HP, Chen, Q, Feng, J, Brunke, MA, Fan, T, Hong, S, Nobre, P, Peano, D, Qin, Y, Vitart, F, Xie, S, Zhan, Y, Klocke, D, Leung, R, Li, X, Ek, M, Guo, W, Balsamo, G, Bao, Q, Chou, SC, De Rosnay, P, Lin, Y, Zhu, Y, Qian, Y, Zhao, P, Tang, J, Liang, XZ, Hong, J, Ji, D, Ji, Z, Qiu, Y, Sugimoto, S, Wang, W, Yang, K, Yu, M, Xue, Y, Xue, Y, Diallo, I, Boone, AA, Yao, T, Zhang, Y, Zeng, X, Neelin, JD, Lau, WKM, Pan, Y, Liu, Y, Pan, X, Tang, Q, Van Oevelen, PJ, Sato, T, Koo, MS, Materia, S, Shi, C, Yang, J, Ardilouze, C, Lin, Z, Qi, X, Nakamura, T, Saha, SK, Senan, R, Takaya, Y, Wang, H, Zhang, H, Zhao, M, Nayak, HP, Chen, Q, Feng, J, Brunke, MA, Fan, T, Hong, S, Nobre, P, Peano, D, Qin, Y, Vitart, F, Xie, S, Zhan, Y, Klocke, D, Leung, R, Li, X, Ek, M, Guo, W, Balsamo, G, Bao, Q, Chou, SC, De Rosnay, P, Lin, Y, Zhu, Y, Qian, Y, Zhao, P, Tang, J, Liang, XZ, Hong, J, Ji, D, Ji, Z, Qiu, Y, Sugimoto, S, Wang, W, Yang, K, and Yu, M

Abstract

Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land-atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the "Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction"(LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global "hotspot"regions identified here; the ensemble means in some "hotspots"produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

Published
2022

18. Data for the consolidated European synthesis of CO2 emissions and removals for EU27 and UK: 1990-2020

Author

McGrath, M., Petrescu, A., Peylin, P., Andrew, R., Matthews, B., Dentener, F., Balkovič, J., Bastrikov, V., Becker, M., Broquet, G., Ciais, P., Fortems, A., Ganzenmüller, R., Grassi, G., Harris, I., Jones, M., Knauer, J., Kuhnert, M., Monteil, G., Munassar, S., Palmer, P., Peters, G., Qiu, C., Schelhaas, M.-J., Tarasova, O., Vizzarri, M., Winkler, K., Balsamo, G., Berchet, A., Briggs, P., Brockmann, P., Chevallier, F., Conchedda, G., Crippa, M., Dellaert, S., Denier van der Gon, H., Filipek, S., Friedlingstein, P., Fuchs, R., Gauss, M., Gerbig, C., Guizzardi, D., Günther, D., Houghton, R., Janssens-Maenhout, G., Lauerwald, R., Lerink, B., Luijkx, I., Moulas, G., Muntean, M., Nabuurs, G.-J., Paquirissamy, A., Perugini, L., Peters, W., Pilli, R., Pongratz, J., Regnier, P., Scholze, M., Serengil, Y., Smith, P., Solazzo, E., Thompson, R., Tubiello, F., Vesala, T., Walther, S., McGrath, M., Petrescu, A., Peylin, P., Andrew, R., Matthews, B., Dentener, F., Balkovič, J., Bastrikov, V., Becker, M., Broquet, G., Ciais, P., Fortems, A., Ganzenmüller, R., Grassi, G., Harris, I., Jones, M., Knauer, J., Kuhnert, M., Monteil, G., Munassar, S., Palmer, P., Peters, G., Qiu, C., Schelhaas, M.-J., Tarasova, O., Vizzarri, M., Winkler, K., Balsamo, G., Berchet, A., Briggs, P., Brockmann, P., Chevallier, F., Conchedda, G., Crippa, M., Dellaert, S., Denier van der Gon, H., Filipek, S., Friedlingstein, P., Fuchs, R., Gauss, M., Gerbig, C., Guizzardi, D., Günther, D., Houghton, R., Janssens-Maenhout, G., Lauerwald, R., Lerink, B., Luijkx, I., Moulas, G., Muntean, M., Nabuurs, G.-J., Paquirissamy, A., Perugini, L., Peters, W., Pilli, R., Pongratz, J., Regnier, P., Scholze, M., Serengil, Y., Smith, P., Solazzo, E., Thompson, R., Tubiello, F., Vesala, T., and Walther, S.

Abstract

The annual carbon dioxide fluxes used to create all graphs in the main text of McGrath et al, "European synthesis of CO2 emissions and removals for EU27 and UK: 1990-2020", submitted to Earth System Science Data.

Published
2022

19. An Urban Scheme for the ECMWF Integrated Forecasting System: Global Forecasts and Residential CO2 Emissions.

Author

McNorton, J., Agustí‐Panareda, A., Arduini, G., Balsamo, G., Bousserez, N., Boussetta, S., Chericoni, M., Choulga, M., Engelen, R., and Guevara, M.

Subjects
NUMERICAL weather forecasting, LAND surface temperature, TRACE gases, URBAN heat islands, WIND forecasting, WEATHER forecasting
Abstract

The impact of urbanization on local weather patterns affects over half the global population. Global numerical weather prediction systems have reached a resolution at which urban conurbations can be spatially resolved, justifying their representation within land surface parameterizations with the aim of improving local predictions. Additionally, real‐time atmospheric monitoring of trace gas emissions can utilize weather variables relevant for urban areas. We investigated whether a simple single‐layer urban canopy scheme can be used within a global forecast model to jointly improve predictions of near‐surface weather variables and residential CO2 emissions. The scheme has been implemented in the Integrated Forecast System used operationally at the European Centre for Medium‐Range Weather Forecasts running at ∼9 km horizontal resolution. First, we selected a suitable urban land cover map (ECOCLIMAP‐SG) based on comparisons with regional data and land surface temperature MODIS retrievals. The urban scheme is verified by providing improved 2 m temperature (∼10%) and 10 m wind (∼17%) RMSE values for both summer and winter months around urban environments. The influence of the scheme was most noticeable at night. Additionally, we have implemented a simple temperature‐dependent residential emissions model to calculate real‐time CO2 heating emissions. These were validated against existing offline products, national reporting and by comparing atmospheric simulations with total column CO2 observations. The results show an improved temporal variability of emissions, which arise from synoptic scale temperature changes. Given the improved predictability from the urban scheme for both weather and emissions, it will be operationally implemented in an upcoming model cycle. Plain Language Summary: In urban areas, temperatures are often elevated due to an effect known as the urban heat island. Although global weather forecasts, generated using computer models, typically include a representation of land surface processes, they often do not include the urban environment. We implemented a relatively simple urban scheme in the model of the European Centre for Medium‐Range Weather Forecasts and selected an appropriate urban cover map to use by comparing forecast land temperatures with satellite observations. We then compared this scheme with observations from urban sites around the globe and found improved temperature and wind forecasts. Furthermore, we used information from the urban scheme to generate a global forecast of residential CO2 emissions from heating. We find that by forecasting these emissions using the weather model we improve our prediction of atmospheric CO2 concentrations around urban environments. Key Points: Several urban land cover maps were evaluated using satellite land‐surface temperature retrievals and independent dataAt ∼9 km horizontal resolution, an urban scheme improves modeled 2 m temperature and 10 m wind forecasts over urban areas of varying sizeAn online residential heating CO2 emissions model (Modeling Emissions from Heating in Near‐real‐time Driven by the Integrated Forecasting System) using model variables improves forecasts of atmospheric concentrations [ABSTRACT FROM AUTHOR]

Published
2023
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28. Capability of the variogram to quantify the spatial patterns of surface fluxes and soil moisture simulated by land surface models

Author

Garrigues, S., Verhoef, A., Blyth, E., Wright, A., Balan-Sarojini, B., Robinson, E.L., Dadson, S., Boone, A., Boussetta, S., Balsamo, G., Garrigues, S., Verhoef, A., Blyth, E., Wright, A., Balan-Sarojini, B., Robinson, E.L., Dadson, S., Boone, A., Boussetta, S., and Balsamo, G.

Abstract

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 geographic domains – 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.

Published
2021

29. Toward an Operational Anthropogenic CO2 Emissions Monitoring and Verification Support Capacity

Author

Doubovik, Oleg, Janssens-Maenhout, G., Pinty, B., Dowell, M., Zunker, H., Andersson, E., Balsamo, G., Bézy, J.-L., Brunhes, T., Bösch, H., Bojkov, B., Brunner, D., Buchwitz, M., Crisp, D., Ciais, P., Counet, P., Dee, D., Denier Van Der Gon, H., Dolman, H., Drinkwater, M., Dubovik, O., Engelen, R., Fehr, T., Fernandez, V., Heimann, M., Holmlund, K., Houweling, S., Husband, R., Juvyns, O., Kentarchos, A., Landgraf, J., Lang, R., Löscher, A., Marshall, J., Meijer, Y., Nakajima, M., Palmer, P., Peylin, P., Rayner, P., Scholze, M., Sierk, B., Tamminen, J., Veefkind, P., Laboratoire d’Optique Atmosphérique - UMR 8518 (LOA), Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Earth Sciences, European Commission - Joint Research Centre [Ispra] (JRC), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), ICOS-ATC (ICOS-ATC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)

Subjects
Atmospheric Science, Decision support system, Service (systems architecture), 010504 meteorology & atmospheric sciences, Agency (philosophy), Climate change, 010501 environmental sciences, 7. Clean energy, 01 natural sciences, 12. Responsible consumption, Atmospheric measurements, SDG 13 - Climate Action, European commission, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], business.industry, [SDE.IE]Environmental Sciences/Environmental Engineering, Environmental resource management, [SDE.ES]Environmental Sciences/Environmental and Society, 13. Climate action, Greenhouse gas, Transparency (graphic), [SDE]Environmental Sciences, Environmental science, business
Abstract

Under the Paris Agreement (PA), progress of emission reduction efforts is tracked on the basis of regular updates to national greenhouse gas (GHG) inventories, referred to as bottom-up estimates. However, only top-down atmospheric measurements can provide observation-based evidence of emission trends. Today, there is no internationally agreed, operational capacity to monitor anthropogenic GHG emission trends using atmospheric measurements to complement national bottom-up inventories. The European Commission (EC), the European Space Agency, the European Centre for Medium-Range Weather Forecasts, the European Organisation for the Exploitation of Meteorological Satellites, and international experts are joining forces to develop such an operational capacity for monitoring anthropogenic CO2 emissions as a new CO2 service under the EC’s Copernicus program. Design studies have been used to translate identified needs into defined requirements and functionalities of this anthropogenic CO2 emissions Monitoring and Verification Support (CO2MVS) capacity. It adopts a holistic view and includes components such as atmospheric spaceborne and in situ measurements, bottom-up CO2 emission maps, improved modeling of the carbon cycle, an operational data-assimilation system integrating top-down and bottom-up information, and a policy-relevant decision support tool. The CO2MVS capacity with operational capabilities by 2026 is expected to visualize regular updates of global CO2 emissions, likely at 0.05° x 0.05°. This will complement the PA’s enhanced transparency framework, providing actionable information on anthropogenic CO2 emissions that are the main driver of climate change. This information will be available to all stakeholders, including governments and citizens, allowing them to reflect on trends and effectiveness of reduction measures. The new EC gave the green light to pass the CO2MVS from exploratory to implementing phase.

Published
2020

30. Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments

Author

Terzago S.[1], Andreoli V.[2], Arduini G.[3], Balsamo G.[3], Campo L.[4], Cassardo C.[2], Cremonese E.[5], Dolia D.[4], Gabellani S.[4], Von Hardenberg J.[1, Morra Di Cella U.[5], Palazzi E.[1], Piazzi G.[4, Pogliotti P.[5], Provenzale A.[8], CNR Institute of Atmospheric Sciences and Climate (ISAC), Consiglio Nazionale delle Ricerche (CNR), University of Torino, Università degli studi di Torino (UNITO), European Centre for Medium-Range Weather Forecasts (ECMWF), CIMA RESEARCH FOUNDATION SAVONA ITA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), ENVIRONMENTAL PROTECTION AGENCY OF AOSTA VALLEY ARPA VALLE D'AOSTA SAINT CHRISTOPHE ITA, DEPARTMENT OF ENVIRONMENT LAND AND INFRASTRUCTURE ENGINEERING POLITECNICO DI TORINO ITA, Hydrosystèmes continentaux anthropisés : ressources, risques, restauration (UR HYCAR), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), and CNR Institute of Geosciences and Earth Resources (IGG)

Subjects
Meteorological forcings, 010504 meteorology & atmospheric sciences, Meteorology, [SDE.MCG]Environmental Sciences/Global Changes, 0207 environmental engineering, alps, 02 engineering and technology, lcsh:Technology, 01 natural sciences, snow model, lcsh:TD1-1066, Weather station, Multivariate interpolation, snow model, snowpack, snow water equivalent, mountains, sensitivity experiment, modèle de neige, Data assimilation, snowpack, snow models, Precipitation, Shortwave radiation, lcsh:Environmental technology. Sanitary engineering, 020701 environmental engineering, lcsh:Environmental sciences, 0105 earth and related environmental sciences, lcsh:GE1-350, lcsh:T, montagne, lcsh:Geography. Anthropology. Recreation, model sensitivity, mountains, Snowpack, Snow, lcsh:G, 13. Climate action, Temporal resolution, snow water equivalent, Environmental science
Abstract

Snow models are usually evaluated at sites providing high-quality meteorological data, so that the uncertainty in the meteorological input data can be neglected when assessing model performances. However, high-quality input data are rarely available in mountain areas and, in practical applications, the meteorological forcing used to drive snow models is typically derived from spatial interpolation of the available in situ data or from reanalyses, whose accuracy can be considerably lower. In order to fully characterize the performances of a snow model, the model sensitivity to errors in the input data should be quantified. In this study we test the ability of six snow models to reproduce snow water equivalent, snow density and snow depth when they are forced by meteorological input data with gradually lower accuracy. The SNOWPACK, GEOTOP, HTESSEL, UTOPIA, SMASH and S3M snow models are forced, first, with high-quality measurements performed at the experimental site of Torgnon, located at 2160 m a.s.l. in the Italian Alps (control run). Then, the models are forced by data at gradually lower temporal and/or spatial resolution, obtained by (i) sampling the original Torgnon 30 min time series at 3, 6, and 12 h, (ii) spatially interpolating neighbouring in situ station measurements and (iii) extracting information from GLDAS, ERA5 and ERA-Interim reanalyses at the grid point closest to the Torgnon site. Since the selected models are characterized by different degrees of complexity, from highly sophisticated multi-layer snow models to simple, empirical, single-layer snow schemes, we also discuss the results of these experiments in relation to the model complexity. The results show that, when forced by accurate 30 min resolution weather station data, the single-layer, intermediate-complexity snow models HTESSEL and UTOPIA provide similar skills to the more sophisticated multi-layer model SNOWPACK, and these three models show better agreement with observations and more robust performances over different seasons compared to the lower-complexity models SMASH and S3M. All models forced by 3-hourly data provide similar skills to the control run, while the use of 6- and 12-hourly temporal resolution forcings may lead to a reduction in model performances if the incoming shortwave radiation is not properly represented. The SMASH model generally shows low sensitivity to the temporal degradation of the input data. Spatially interpolated data from neighbouring stations and reanalyses are found to be adequate forcings, provided that temperature and precipitation variables are not affected by large biases over the considered period. However, a simple bias-adjustment technique applied to ERA-Interim temperatures allowed all models to achieve similar performances to the control run. Regardless of their complexity, all models show weaknesses in the representation of the snow density.

Published
2020

31. Interactions Between the Amazonian Rainforest and Cumuli Clouds: A Large‐Eddy Simulation, High‐Resolution ECMWF, and Observational Intercomparison Study

Author

Vilà‐Guerau De Arellano, J., Wang, Xuemei, Pedruzo-Bagazgoitia, X., Sikma, M., Agustí-Panareda, A., Boussetta, S., Balsamo, G., Machado, L.A.T., Biscaro, T., Gentine, P., Martin, S.T., Fuentes, J.D., Gerken, T., Vilà‐Guerau De Arellano, J., Wang, Xuemei, Pedruzo-Bagazgoitia, X., Sikma, M., Agustí-Panareda, A., Boussetta, S., Balsamo, G., Machado, L.A.T., Biscaro, T., Gentine, P., Martin, S.T., Fuentes, J.D., and Gerken, T.

Abstract

The explicit coupling at meter and second scales of vegetation's responses to the atmospheric‐boundary layer dynamics drives a dynamic heterogeneity that influences canopy‐top fluxes and cloud formation. Focusing on a representative day during the Amazonian dry season, we investigate the diurnal cycle of energy, moisture and carbon dioxide at the canopy top, and the transition from clear to cloudy conditions. To this end, we compare results from a large‐eddy simulation technique, a high‐resolution global weather model, and a complete observational data set collected during the GoAmazon14/15 campaign. The overall model‐observation comparisons of radiation and canopy‐top fluxes, turbulence, and cloud dynamics are very satisfactory, with all the modeled variables lying within the standard deviation of the monthly aggregated observations. Our analysis indicates that the timing of the change in the daylight carbon exchange, from a sink to a source, remains uncertain and is probably related to the stomata closure caused by the increase in vapor pressure deficit during the afternoon. We demonstrate quantitatively that heat and moisture transport from the subcloud layer into the cloud layer are misrepresented by the global model, yielding low values of specific humidity and thermal instability above the cloud base. Finally, the numerical simulations and observational data are adequate settings for benchmarking more comprehensive studies of plant responses, microphysics, and radiation

Published
2020

33. Interactions Between the Amazonian Rainforest and Cumuli Clouds: A Large‐Eddy Simulation, High‐Resolution ECMWF, and Observational Intercomparison Study

Author

Vilà‐Guerau de Arellano, J., primary, Wang, X., additional, Pedruzo‐Bagazgoitia, X., additional, Sikma, M., additional, Agustí‐Panareda, A., additional, Boussetta, S., additional, Balsamo, G., additional, Machado, L. A. T., additional, Biscaro, T., additional, Gentine, P., additional, Martin, S. T., additional, Fuentes, J. D., additional, and Gerken, T., additional

Published
2020
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34. Evaluation of Global Observations-Based Evapotranspiration Datasets and IPCC AR4 Simulations

Author

Mueller, B, Seneviratne, S. I, Jimenez, C, Corti, T, Hirschi, M, Balsamo, G, Ciais, P, Dirmeyer, P, Fisher, J. B, Guo, Z, Jung, M, Maignan, F, McCabe, M. F, Reichle, R, Reichstein, M, Rodell, M, Sheffield, J, Teuling, A. J, Wang, K, Wood, E. F, and Zhang, Y

Subjects
Geophysics
Abstract

Quantification of global land evapotranspiration (ET) has long been associated with large uncertainties due to the lack of reference observations. Several recently developed products now provide the capacity to estimate ET at global scales. These products, partly based on observational data, include satellite ]based products, land surface model (LSM) simulations, atmospheric reanalysis output, estimates based on empirical upscaling of eddycovariance flux measurements, and atmospheric water balance datasets. The LandFlux-EVAL project aims to evaluate and compare these newly developed datasets. Additionally, an evaluation of IPCC AR4 global climate model (GCM) simulations is presented, providing an assessment of their capacity to reproduce flux behavior relative to the observations ]based products. Though differently constrained with observations, the analyzed reference datasets display similar large-scale ET patterns. ET from the IPCC AR4 simulations was significantly smaller than that from the other products for India (up to 1 mm/d) and parts of eastern South America, and larger in the western USA, Australia and China. The inter-product variance is lower across the IPCC AR4 simulations than across the reference datasets in several regions, which indicates that uncertainties may be underestimated in the IPCC AR4 models due to shared biases of these simulations.

Published
2011
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35. Global Intercomparison of 12 Land Surface Heat Flux Estimates

Author

Jimenez, C, Prigent, C, Mueller, B, Seneviratne, S. I, McCabe, M. F, Wood, E. F, Rossow, W. B, Balsamo, G, Betts, A. K, Dirmeyer, P. A, Fisher, J. B, Jung, M, Kanamitsu, M, Reichle, R. H, Reichstein, M, Rodell, M, Sheffield, J, Tu, K, and Wang, K

Subjects
Geophysics
Abstract

A global intercomparison of 12 monthly mean land surface heat flux products for the period 1993-1995 is presented. The intercomparison includes some of the first emerging global satellite-based products (developed at Paris Observatory, Max Planck Institute for Biogeochemistry, University of California Berkeley, University of Maryland, and Princeton University) and examples of fluxes produced by reanalyses (ERA-Interim, MERRA, NCEP-DOE) and off-line land surface models (GSWP-2, GLDAS CLM/ Mosaic/Noah). An intercomparison of the global latent heat flux (Q(sub le)) annual means shows a spread of approx 20 W/sq m (all-product global average of approx 45 W/sq m). A similar spread is observed for the sensible (Q(sub h)) and net radiative (R(sub n)) fluxes. In general, the products correlate well with each other, helped by the large seasonal variability and common forcing data for some of the products. Expected spatial distributions related to the major climatic regimes and geographical features are reproduced by all products. Nevertheless, large Q(sub le)and Q(sub h) absolute differences are also observed. The fluxes were spatially averaged for 10 vegetation classes. The larger Q(sub le) differences were observed for the rain forest but, when normalized by mean fluxes, the differences were comparable to other classes. In general, the correlations between Q(sub le) and R(sub n) were higher for the satellite-based products compared with the reanalyses and off-line models. The fluxes were also averaged for 10 selected basins. The seasonality was generally well captured by all products, but large differences in the flux partitioning were observed for some products and basins.

Published
2011
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38. Streamflows over a West African Basin from the ALMIP2 Model Ensemble

Author

Getirana, Augusto, Boone, Aaron, Peugeot, Christophe, Ait-Mesbah, S., Polcher, J., Anderson, M., Balsamo, G., Boussetta, S., Dutra, E., Pappenberger, F., Hain, C., Favot, F., Guichard, F., Kaptue, A., Cappelaere, B., Demarty, Jérôme, Seguis, L., Chaffard, V., Cohard, J. M., Gascon, T., Galle, S., Hector, B., Lebel, T., Pellarin, T., Richard, A., Quantin, G., Vischel, T., Chan, E., Verseghy, D., Ducharne, Agnès, Magand, C., Grippa, Manuela, Hiernaux, Pierre, Kergoat, Laurent, Pierre, C., Nasonova, Y. Gusev O., Harris, P., He, X., Yorozu, K., Kotsuki, S., Tanaka, K., Kim, H., Oki, T., Kumar, S., Lo, M.-H., Mahanama, S., Maignan, F., Ottlé, C., Mamadou, O., Shmakin, A., Sokratov, V., Turkov, D., Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut de Recherche pour le Développement (IRD), Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), and Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut de Recherche pour le Développement (IRD)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Hydrology, Atmospheric Science, geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Hydrological modelling, 0208 environmental biotechnology, Mesoscale meteorology, Drainage basin, 02 engineering and technology, Groundwater recharge, Infiltration (HVAC), Monsoon, 01 natural sciences, 6. Clean water, 020801 environmental engineering, Climatology, Streamflow, Environmental science, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, Surface runoff, 0105 earth and related environmental sciences
Abstract

Comparing streamflow simulations against observations has become a straightforward way to evaluate a land surface model’s (LSM) ability in simulating water budget within a catchment. Using a mesoscale river routing scheme (RRS), this study evaluates simulated streamflows over the upper Ouémé River basin resulting from 14 LSMs within the framework of phase 2 of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project (ALMIP2). The ALMIP2 RRS (ARTS) has been used to route LSM outputs. ARTS is based on the nonlinear Muskingum–Cunge method and a simple deep water infiltration formulation representing water-table recharge as previously observed in that region. Simulations are performed for the 2005–08 period during which ground observations are largely available. Experiments are designed using different ground-based rainfall datasets derived from two interpolation methods: the Thiessen technique and a combined kriging–Lagrangian methodology. LSM-based total runoff (TR) averages vary from 0.07 to 1.97 mm day−1, while optimal TR was estimated as ~0.65 mm day−1. This highly affected the RRS parameterization and streamflow simulations. Optimal Nash–Sutcliffe coefficients for LSM-averaged streamflows varied from 0.66 to 0.92, depending on the gauge station. However, individual LSM performances show a wider range. A more detailed rainfall distribution provided by the kriging–Lagrangian methodology resulted in overall better streamflow simulations. The early runoff generation related to reduced infiltration rates during early rainfall events features as one of the main reasons for poor LSM performances.

Published
2017

39. Modeling Surface Runoff and Water Fluxes over Contrasted Soils in the Pastoral Sahel : Evaluation of the ALMIP2 Land Surface Models over the Gourma Region in Mali

Author

Grippa, Manuela, Gal, Laetitia, Anderson, Martha, Ait-Mesbah, S., Polcher, Jan, Balsamo, G., Boussetta, S., Dutra, Emanuel, Pappenberger, F., Hain, Christopher, Boone, Aaron, Favot, F., Guichard, F., Kaptue, A., Roujean, Jean-Louis, Cappelaere, Bernard, Demarty, Jérôme, Peugeot, Christophe, Séguis, L., Velluet, C., Chaffard, V., Cohard, J. M., Gascon, T., Galle, S., Hector, B., Lebel, T., Pellarin, T., Richard, A., Quantin, G., Vischel, T., Chan, E., Verseghy, D., Ducharne, Agnès, Magand, C., Getirana, A., Hiernaux, Pierre, Kergoat, Laurent, Mougin, Éric, Pierre, Caroline, Gusev, Y., Nasonova, O., Harris, P., He, X., Yorozu, K., Kotsuki, S., Tanaka, K., Kim, H., Oki, T., Kumar, S., Lo, M.-H., Mahanama, S., Maignan, F., Ottlé, C., Mamadou, O., Shmakin, A., Sokratov, V., Turkov, D., Working Group, Almip2, Géosciences Environnement Toulouse (GET), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), United States Department of Agriculture, European Centre for Medium-Range Weather Forecasts (ECMWF), Laboratoire de mesure du carbone 14 (LMC14 - UMS 2572), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut de Radioprotection et de Sûreté Nucléaire (IRSN)-Ministère de la Culture et de la Communication (MCC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Structure et fonctionnement des systèmes hydriques continentaux (SISYPHE), Université Pierre et Marie Curie - Paris 6 (UPMC)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Centre d'études spatiales de la biosphère (CESBIO), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut de Recherche pour le Développement (IRD)-Centre National d'Études Spatiales [Toulouse] (CNES), Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-École pratique des hautes études (EPHE)-MINES ParisTech - École nationale supérieure des mines de Paris-Centre National de la Recherche Scientifique (CNRS), NASA Goddard Space Flight Center (GSFC), Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Météo France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-MINES ParisTech - École nationale supérieure des mines de Paris, Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Météo France-Centre National de la Recherche Scientifique (CNRS), Earth Systems Science Interdisciplinary Center, University of Maryland [College Park], University of Maryland System-University of Maryland System, and Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, 0208 environmental biotechnology, Mesoscale meteorology, 02 engineering and technology, Monsoon, 01 natural sciences, Evapotranspiration, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, 2. Zero hunger, Hydrology, Multidisciplinary analysis, 15. Life on land, [SDE.ES]Environmental Sciences/Environmental and Society, 6. Clean water, 020801 environmental engineering, Water resources, 13. Climate action, Soil water, [SDE]Environmental Sciences, Environmental science, Surface runoff, Surface water
Abstract

Land surface processes play an important role in the West African monsoon variability. In addition, the evolution of hydrological systems in this region, and particularly the increase of surface water and runoff coefficients observed since the 1950s, has had a strong impact on water resources and on the occurrence of floods events. This study addresses results from phase 2 of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project (ALMIP2), carried out to evaluate the capability of different state-of-the-art land surface models to reproduce surface processes at the mesoscale. Evaluation of runoff and water fluxes over the Mali site is carried out through comparison with runoff estimations over endorheic watersheds as well as evapotranspiration (ET) measurements. Three remote-sensing-based ET products [ALEXI, MODIS, and Global Land Evaporation Amsterdam Model (GLEAM)] are also analyzed. It is found that, over deep sandy soils, surface runoff is generally overestimated, but the ALMIP2 multimodel mean reproduces in situ measurements of ET and water stress events rather well. However, ALMIP2 models are generally unable to distinguish among the two contrasted hydrological systems typical of the study area. Employing as input a soil map that explicitly represents shallow soils improves the representation of water fluxes for the models that can account for their representation. Shallow soils are shown to be also quite challenging for remote-sensing-based ET products, even if their effect on evaporative loss was captured by the diagnostic thermal-based ALEXI. A better representation of these soils, in soil databases, model parameterizations, and remote sensing algorithms, is fundamental to improve the estimation of water fluxes in this part of the Sahel.

Published
2017

40. Observing Mass Transport to Understand Global Change and and to benefit Society : Science and User Needs - An international multi-disciplinary initiative for IUGG

Author

Pail, R., Bingham, R., Braitenberg, Carla, Eicker, A., Horwath, M., Ivins, E., Longuevergne, L., Panet, I., Wouters, B., Balsamo, G., Becker, M., Bertrand, D., Bolten, J. D., Boy, J. P., van den Broeke, M., Cazenave, A., Chambers, D., van Dam, T., Diament, M., van Dijk, A., Dobslaw, H., Döll, P., Ebbing, J., Famiglietti, J., Feng, W., Forsberg, R., van de Giesen, N., Greff, M., Güntner, A., Guo, J. Y., Han, S. C., Hanna, E., Heki, K., Hetenyi, G., Jayne, S., Jiang, W., Jin, S., Kaser, G., King, M., Köhl, A., Kunstmann, H., Kusche, J., Lay, T., Löcher, A., Lutchke, S., Marcos, M., van der Meijde, M., Mikhailov, V., Ohlwein, C., Pollitz, F., Pokhrel, Y., Ponte, R., Rodell, M., Rolstad Denby, C., Save, H., Scanlon, B., Seneviratne, S., Seyler, F., Shepherd, A., Song, T., Spakman, W., Shum, C. K., Steffen, H., Sun, W., Tang, Q., Tiwari, V., Velicogna, I., Wahr, J., van der Wal, W., Wang, L., Xie, H., Yeh, H. C., Yeh, P., Zaitchik, B., Zlotnicki, V., Pail, R., Bingham, R., Braitenberg, Carla, Eicker, A., Horwath, M., Ivins, E., Longuevergne, L., Panet, I., Wouters, B., Balsamo, G., Becker, M., Bertrand, D., Bolten, J. D., Boy, J. P., van den Broeke, M., Cazenave, A., Chambers, D., van Dam, T., Diament, M., van Dijk, A., Dobslaw, H., Döll, P., Ebbing, J., Famiglietti, J., Feng, W., Forsberg, R., van de Giesen, N., Greff, M., Güntner, A., Guo, J. Y., Han, S. C., Hanna, E., Heki, K., Hetenyi, G., Jayne, S., Jiang, W., Jin, S., Kaser, G., King, M., Köhl, A., Kunstmann, H., Kusche, J., Lay, T., Löcher, A., Lutchke, S., Marcos, M., van der Meijde, M., Mikhailov, V., Ohlwein, C., Pollitz, F., Pokhrel, Y., Ponte, R., Rodell, M., Rolstad Denby, C., Save, H., Scanlon, B., Seneviratne, S., Seyler, F., Shepherd, A., Song, T., Spakman, W., Shum, C. K., Steffen, H., Sun, W., Tang, Q., Tiwari, V., Velicogna, I., Wahr, J., van der Wal, W., Wang, L., Xie, H., Yeh, H. C., Yeh, P., Zaitchik, B., and Zlotnicki, V.

Subjects
Mass transport, Mass transport, Earth system science, Satellite gravimetry, Sustained observation, Climate change, Sustained observation, Climate change, Satellite gravimetry, Earth system science
Abstract

Satellite gravimetry is a unique measurement technique for observing mass transport processes in the Earth system on a global scale, providing essential indicators of both subtle and dramatic global change. Although past and current satellite gravity missions have achieved spectacular science results, due to their limited spatial and temporal resolution as well as limited length of the available time series numerous important questions are still unresolved. Therefore, it is important to move from current demonstration capabilities to sustained observation of the Earth’s gravity field. In an international initiative performed under the umbrella of the International Union of Geodesy and Geophysics, consensus on the science and user needs for a future satellite gravity observing system has been derived by an international panel of scientists representing the main fields of application, i.e., continental hydrology, cryosphere, ocean, atmosphere and solid Earth. In this paper the main results and findings of this initiative are summarized. The required target performance in terms of equivalent water height has been identified as 5 cm for monthly fields and 0.5 cm/year for long-term trends at a spatial resolution of 150 km. The benefits to meet the main scientific and societal objectives are investigated, and the added value is demonstrated for selected case studies covering the main fields of application. The resulting consolidated view on the required performance of a future sustained satellite gravity observing system represents a solid basis for the definition of technological and mission requirements, and is a prerequisite for mission design studies of future mission concepts and constellations.

Published
2015

41. Toward an Operational Anthropogenic CO2 Emissions Monitoring and Verification Support Capacity.

Author

Janssens-Maenhout, G., Pinty, B., Dowell, M., Zunker, H., Andersson, E., Balsamo, G., Bézy, J.-L., Brunhes, T., Bösch, H., Bojkov, B., Brunner, D., Buchwitz, M., Crisp, D., Ciais, P., Counet, P., Dee, D., van der Gon, H. Denier, Dolman, H., Drinkwater, M. R., and Dubovik, O.

Subjects
EMISSION inventories, METEOROLOGICAL satellites, CARBON cycle, GREENHOUSE gases, CLIMATE change
Abstract

Under the Paris Agreement (PA), progress of emission reduction efforts is tracked on the basis of regular updates to national greenhouse gas (GHG) inventories, referred to as bottomup estimates. However, only top-down atmospheric measurements can provide observation-based evidence of emission trends. Today, there is no internationally agreed, operational capacity to monitor anthropogenic GHG emission trends using atmospheric measurements to complement national bottom-up inventories. The European Commission (EC), the European Space Agency, the European Centre for Medium-Range Weather Forecasts, the European Organisation for the Exploitation of Meteorological Satellites, and international experts are joining forces to develop such an operational capacity for monitoring anthropogenic CO2 emissions as a new CO2 service under the EC's Copernicus program. Design studies have been used to translate identified needs into defined requirements and functionalities of this anthropogenic CO2 emissions Monitoring and Verification Support (CO2MVS) capacity. It adopts a holistic view and includes components such as atmospheric spaceborne and in situ measurements, bottom-up CO2 emission maps, improved modeling of the carbon cycle, an operational data-assimilation system integrating top-down and bottom-up information, and a policy-relevant decision support tool. The CO2MVS capacity with operational capabilities by 2026 is expected to visualize regular updates of global CO2 emissions, likely at 0.05° x 0.05°. This will complement the PA's enhanced transparency framework, providing actionable information on anthropogenic CO2 emissions that are the main driver of climate change. This information will be available to all stakeholders, including governments and citizens, allowing them to reflect on trends and effectiveness of reduction measures. The new EC gave the green light to pass the CO2MVS from exploratory to implementing phase. [ABSTRACT FROM AUTHOR]

Published
2020
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42. Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

Author

Alessandri, A., Catalano, F., De Felice, M., van den Hurk, B.J.J.M., Doblas Reyes, F., Boussetta, S., Balsamo, G., Miller, P.A., Alessandri, A., Catalano, F., De Felice, M., van den Hurk, B.J.J.M., Doblas Reyes, F., Boussetta, S., Balsamo, G., and Miller, P.A.

Abstract

The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (twentieth century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multiscale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middleto- high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented timevarying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2 m temperature and rainfall are also shown over transiti

Published
2017
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43. The Plumbing of Land Surface Models: Benchmarking Model Performance

Author

Vuichard, And, Best, M., Abramowitz, G., Johnson, H., Pitman, A., Balsamo, G., Boone, A., Cuntz, M., Decharme, B., Dirmeyer, P., Dong, J., EK, M., Guo, Z., Haverd, V., Van Den Hurk, B., Nearing, G., Pak, B., Peters-Lidard, C., Santanello, J., Stevens, L., Vuichard, N., Department Computational Hydrosystems [UFZ Leipzig], Helmholtz Centre for Environmental Research (UFZ), Joint DECC/Defra Met Office Hadley Centre Climate Programme CA01101Australian Research CouncilCE110001028United States Department of Energy (DOE)DE-FG02-04ER63917DE-FG02-04ER63911CFCAS Natural Sciences and Engineering Research Council of Canada (NSERC) BIOCAP CGIAR Natural Resources Canada European Commission FAO-GTOS-TCO iLEAPS Max Planck Institute for Biogeochemistry National Science Foundation (NSF) Tuscia University Universite Laval and Environment Canada United States Department of Energy (DOE, United Kingdom Met Office [Exeter], University of New South Wales [Sydney] (UNSW), European Centre for Medium-Range Weather Forecasts (ECMWF), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), George Mason University [Fairfax], Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), NASA Goddard Space Flight Center (GSFC), GSFC Hydrospheric and Biospheric Sciences Laboratory, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Water and Climate Risk, Amsterdam Global Change Institute, Météo France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Climate Change Research Centre [Sydney] (CCRC), Météo France-Centre National de la Recherche Scientifique (CNRS), and Royal Netherlands Meteorological Institute (KNMI)

Subjects
Surface (mathematics), Atmospheric Science, Meteorology, Climate Models, Parameterization, Project, Sensible heat, [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Latent heat, Range (statistics), Energy partitioning, Shortwave radiation, SDG 7 - Affordable and Clean Energy, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, Energy, Water, Model comparison, Benchmarking, Model evaluation/performance, Impact, Phase, [SDE]Environmental Sciences, Environmental science, Soil-moisture, Land surface model, Hydrology, Atmosphere Coupling Experiment, Nonlinear regression, Simulation
Abstract

The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) was designed to be a land surface model (LSM) benchmarking intercomparison. Unlike the traditional methods of LSM evaluation or comparison, benchmarking uses a fundamentally different approach in that it sets expectations of performance in a range of metrics a priori—before model simulations are performed. This can lead to very different conclusions about LSM performance. For this study, both simple physically based models and empirical relationships were used as the benchmarks. Simulations were performed with 13 LSMs using atmospheric forcing for 20 sites, and then model performance relative to these benchmarks was examined. Results show that even for commonly used statistical metrics, the LSMs’ performance varies considerably when compared to the different benchmarks. All models outperform the simple physically based benchmarks, but for sensible heat flux the LSMs are themselves outperformed by an out-of-sample linear regression against downward shortwave radiation. While moisture information is clearly central to latent heat flux prediction, the LSMs are still outperformed by a three-variable nonlinear regression that uses instantaneous atmospheric humidity and temperature in addition to downward shortwave radiation. These results highlight the limitations of the prevailing paradigm of LSM evaluation that simply compares an LSM to observations and to other LSMs without a mechanism to objectively quantify the expectations of performance. The authors conclude that their results challenge the conceptual view of energy partitioning at the land surface.

Published
2015

44. Observing Mass Transport to Understand Global Change and to Benefit Society: Science and User Needs - An international multi-disciplinary initiative for IUGG

Author

Balsamo, G., Becker, M., Bertrand, D., Bingham, R., Bolten, J., Boy, J., Braitenberg, C., v. d. Broeke, M., Cazenave, A., Chambers, D., van Dam, T., Diament, M., v. Dijk, A., Dobslaw, H., Döll, P., Ebbing, J., Eicker, A., Famiglietti, J., Feng, W., Forsberg, R., v. d. Giesen, N., Greff, M., Güntner, A., Guo, J., Han, S., Hanna, E., Heki, K., Hetényi, G., Horwath, M., Ivins, E., Jayne, S., Jiang, W., Jin, S., Kaser, G., King, M., Köhl, A., Kunstmann, H., Kusche, J., Lay, T., Löcher, A., Longuevergne, L., Luthcke, S., Marcos, M., v. d. Meijde, M., Mikhailov, V., Ohlwein, C., Panet, I., Pokhrel, Y., Pollitz, F., Ponte, R., Rodell, M., Rolstad-Denby, C., Save, H., Scanlon, B., Seneviratne, S., Seyler, F., Shepherd, A., Shum, C., Song, T., Spakman, W., Steffen, H., Sun, W., Tang, Q., Tiwari, V., Velicogna, I., Wahr, J., v. d. Wal, W., Wang, L., Wouters, B., Xie, H., Yeh, H., Yeh, P., Zaitchik, B., Zlotnicki, V., and Pail, R.

Published
2015

45. The plumbing of land surface models: Is poor performance a result of methodology or data quality?

Author

Haughton, N, Abramowitz, G, Pitman, AJ, Or, D, Best, MJ, Johnson, HR, Balsamo, G, Boone, A, Cuntz, M, Decharme, B, Dirmeyer, PA, Dong, J, Ek, M, Guo, Z, Haverd, V, van den Hurk, BJJ, Nearing, GS, Pak, B, Santanello, JA, Stevens, LE, Vuichard, N, Haughton, N, Abramowitz, G, Pitman, AJ, Or, D, Best, MJ, Johnson, HR, Balsamo, G, Boone, A, Cuntz, M, Decharme, B, Dirmeyer, PA, Dong, J, Ek, M, Guo, Z, Haverd, V, van den Hurk, BJJ, Nearing, GS, Pak, B, Santanello, JA, Stevens, LE, and Vuichard, N

Abstract

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.

Published
2016

46. The plumbing of land surface models: Is poor performance a result of methodology or data quality?

Author

Haughton, N., Abramowitz, G., Pitman, A.J., Or, D., Best, M.J., Johnson, H.R., Balsamo, G., Boone, A., Cuntz, Matthias, Decharme, B., Dirmeyer, P.A., Dong, J., Ek, M., Guo, Z., Haverd, V., van den Hurk, B.J.J., Nearing, G.S., Pak, B., Santanello, J.A., Stevens, L.E., Vuichard, N., Haughton, N., Abramowitz, G., Pitman, A.J., Or, D., Best, M.J., Johnson, H.R., Balsamo, G., Boone, A., Cuntz, Matthias, Decharme, B., Dirmeyer, P.A., Dong, J., Ek, M., Guo, Z., Haverd, V., van den Hurk, B.J.J., Nearing, G.S., Pak, B., Santanello, J.A., Stevens, L.E., and Vuichard, N.

Abstract

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.

Published
2016

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