A new diagnostic metric based on soil moisture bimodality is developed in order to examine and compare soil moisture from satellite observations and Earth System Models. The methodology to derive this diagnostic is based on maximum likelihood estimator encoded into an iterative algorithm, which is applied to the soil moisture probability density function. This metric is applied to satellite data from the Advanced Microwave Scanning Radiometer for the Earth Observing System and global climate models data from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Results show high soil moisture bimodality in transitional climate areas and high latitudes, potentially associated with land\uffe2\uff80\uff90atmosphere feedback processes. When comparing satellite versus climate models, a clear difference in their soil moisture bimodality is observed, with systematically higher values in the case of CMIP5 models. These differences appear related to areas where land\uffe2\uff80\uff90atmospheric feedback may be overestimated in current climate models.
", "keywords": ["PREFERENTIAL STATES", "IMPACT", "MIXTURE", "SCHEME", "0207 environmental engineering", "NORMAL-DISTRIBUTIONS", "02 engineering and technology", "15. Life on land", "01 natural sciences", "PART I", "satellite soil moisture", "climate models", "13. Climate action", "Earth and Environmental Sciences", "LAND-SURFACE MODEL", "PRECIPITATION", "SDG 13 - Climate Action", "CMIP5", "ATMOSPHERE COUPLING EXPERIMENT", "land-atmosphere interactions", "soil moisture", "bimodality", "SYSTEM", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016JD026099"}, {"href": "https://doi.org/10.1002/2016JD026099"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Atmospheres", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/2016JD026099", "name": "item", "description": "10.1002/2016JD026099", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/2016JD026099"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-04-27T00:00:00Z"}}, {"id": "10.1016/j.oneear.2020.07.009", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-27T16:17:03Z", "type": "Journal Article", "created": "2020-08-21", "title": "How Simulations of the Land Carbon Sink Are Biased by Ignoring Fluvial Carbon Transfers: A Case Study for the Amazon Basin", "description": "Summary Land-surface models are important tools for simulation of the past, present, and future capacity of terrestrial ecosystems to absorb anthropogenic CO2 emissions. However, fluvial carbon (C) transfers are presently neglected in these models. Using the Amazon basin as a case study, we show that this negligence leads to significant underestimation of the net uptake of atmospheric C while terrestrial C storage changes are overestimated. These biases arise from the fact that C\u2014in reality, leached from soils and exported through the river network\u2014is instead represented as partly being respired and partly being stored in soils. Moreover, these biases scale mainly to the fluvial C export to the coast, despite aquatic CO2 emission to the atmosphere being the major pathway of riverine C exports. We further show that fluvial C transfers may change significantly in response to changes in either hydrology or in atmospheric C uptake by vegetation.", "keywords": ["[SDE] Environmental Sciences", "NEE", "550", "0207 environmental engineering", "G\u00e9n\u00e9ralit\u00e9s", "02 engineering and technology", "910", "15. Life on land", "01 natural sciences", "rivers", "land-surface modeling", "13. Climate action", "[SDE]Environmental Sciences", "carbon cycle", "NBP", "Amazon river", "fluvial carbon fluxes", "land carbon sink", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://dipot.ulb.ac.be/dspace/bitstream/2013/315237/1/doi_298881.pdf"}, {"href": "https://doi.org/10.1016/j.oneear.2020.07.009"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/One%20Earth", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.oneear.2020.07.009", "name": "item", "description": "10.1016/j.oneear.2020.07.009", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.oneear.2020.07.009"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-01T00:00:00Z"}}, {"id": "10.5194/bg-16-4851-2019", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-27T16:22:40Z", "type": "Journal Article", "created": "2019-12-20", "title": "\"Global biosphere\u2013climate interaction: a causal appraisal of observations and models over multiple temporal scales\"", "description": "Abstract. Improving the skill of Earth system models (ESMs) in representing climate\uffe2\uff80\uff93vegetation interactions is crucial to enhance our predictions of future climate and ecosystem functioning. Therefore, ESMs need to correctly simulate the impact of climate on vegetation, but likewise feedbacks of vegetation on climate must be adequately represented. However, model predictions at large spatial scales remain subjected to large uncertainties, mostly due to the lack of observational patterns to benchmark them. Here, the bidirectional nature of climate\uffe2\uff80\uff93vegetation interactions is explored across multiple temporal scales by adopting a spectral Granger causality framework that allows identification of potentially co-dependent variables. Results based on global and multi-decadal records of remotely sensed leaf area index (LAI) and observed atmospheric data show that the climate control on vegetation variability increases with longer temporal scales, being higher at inter-annual than multi-month scales. Globally, precipitation is the most dominant driver of vegetation at monthly scales, particularly in (semi-)arid regions. The seasonal LAI variability in energy-driven latitudes is mainly controlled by radiation, while air temperature controls vegetation growth and decay in high northern latitudes at inter-annual scales. These observational results are used as a benchmark to evaluate four ESM simulations from the Coupled Model Intercomparison Project Phase\uffc2\uffa05 (CMIP5). Findings indicate a tendency of ESMs to over-represent the climate control on LAI dynamics and a particular overestimation of the dominance of precipitation in arid and semi-arid regions at inter-annual scales. Analogously, CMIP5 models overestimate the control of air temperature on seasonal vegetation variability, especially in forested regions. Overall, climate impacts on LAI are found to be stronger than the feedbacks of LAI on climate in both observations and models; in other words, local climate variability leaves a larger imprint on temporal LAI dynamics than vice versa. Note however that while vegetation reacts directly to its local climate conditions, the spatially collocated character of the analysis does not allow for the identification of remote feedbacks, which might result in an underestimation of the biophysical effects of vegetation on climate. Nonetheless, the widespread effect of LAI variability on radiation, as observed over the northern latitudes due to albedo changes, is overestimated by the CMIP5 models. Overall, our experiments emphasise the potential of benchmarking the representation of particular interactions in online ESMs using causal statistics in combination with observational data, as opposed to the more conventional evaluation of the magnitude and dynamics of individual variables.
", "keywords": ["0301 basic medicine", "Evolution", "LAND-SURFACE MODELS", "01 natural sciences", "RECENT TRENDS", "03 medical and health sciences", "Behavior and Systematics", "Life", "QH501-531", "NET PRIMARY PRODUCTION", "QH540-549.5", "Earth-Surface Processes", "0105 earth and related environmental sciences", "QE1-996.5", "EARTH SYSTEM MODEL", "Ecology", "LEAF-AREA INDEX", "Biology and Life Sciences", "Geology", "15. Life on land", "DATA SETS", "13. Climate action", "Earth and Environmental Sciences", "FEEDBACKS", "CO2", "VEGETATION", "SENSITIVITY"]}, "links": [{"href": "https://bg.copernicus.org/articles/16/4851/2019/bg-16-4851-2019.pdf"}, {"href": "https://doi.org/10.5194/bg-16-4851-2019"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-16-4851-2019", "name": "item", "description": "10.5194/bg-16-4851-2019", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-16-4851-2019"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-12-20T00:00:00Z"}}, {"id": "11388/369610", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:26:14Z", "type": "Journal Article", "created": "2025-09-21", "title": "Vegetation type and climate determine temperature thresholds of soil respiration across drylands", "description": "Closed AccessPeer reviewed", "keywords": ["Soil respiration Water-limited ecosystems Forest Shrubland Grassland Soil temperature and moisture thresholds Plant and microbial community adaptation Land-surface models", "Soil temperature and moisture thresholds", "Water-limited ecosystems", "Forest", "Soil respiration", "Grassland", "Plant and microbial community adaptation", "Land-surface models", "Shrubland"], "contacts": [{"organization": "Mar\u00eda Almagro, Ana Rey, Rosa M. Incl\u00e1n, Josep Barba, Rodrigo Vargas, Arnaud Carrara, Jos\u00e9 M. Gr\u00fcnzweig, Marcelo Sternberg, Yiftach Talmon, Rebecca L. McCulley, Sara Mara\u00f1\u00f3n-Jim\u00e9nez, Pen\u00e9lope Serrano-Ortiz, Javier Mart\u00ednez-L\u00f3pez, Carme Estruch, Gabriele Guidolotti, Chao-Ting Chang, Joan Llovet, Mauro Lo Cascio, Jorge F. Perez-Quezada, Alexandra C. Correia, Jo\u00e3o Banza, Mar\u00eda C. Caldeira, Carla Nogueira, Miguel N. Bugalho, Mariah S. Carbone, Mar\u00eda Mart\u00ednez-Mena, Simone Mereu, Jorge Curiel Yuste,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/11388/369610"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Biology%20and%20Biochemistry", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11388/369610", "name": "item", "description": "11388/369610", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11388/369610"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-12-01T00:00:00Z"}}, {"id": "1871.1/6a108faf-cde4-46ae-ada2-63182a618c9f", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-27T16:26:30Z", "type": "Journal Article", "created": "2025-10-23", "title": "Demography, dynamics and data: building confidence for simulating changes in the world's forests", "description": "Summary
Vegetation demographic models (VDMs) are advanced tools for simulating forest responses to climate and land\uffe2\uff80\uff90use changes, and are essential for projecting carbon cycling and large\uffe2\uff80\uff90scale forest management strategies. Despite their increasing incorporation into Earth System Models, VDMs differ in their demographic assumptions, with no prior quantitative comparison of their performance.
We benchmarked nine VDMs against observational data from boreal, temperate and tropical sites, assessing their accuracy in predicting tree growth, carbon turnover, biomass stocks and size distributions. Models were simulated under consistent climate conditions with postdisturbance recovery monitored for at least 420\uffe2\uff80\uff89yr.
Postdisturbance carbon recovery trajectories showed significant variability while remaining within observational ranges. Initial regrowth rates varied substantially (0.03\uffe2\uff80\uff930.60, 0.18\uffe2\uff80\uff930.70 and 0.35\uffe2\uff80\uff931.10 kgCm \uffe2\uff88\uff922 \uffe2\uff80\uff89yr \uffe2\uff88\uff921 for boreal, temperate and tropical sites, respectively), influenced by each model's initial forest state. Models captured mature forest carbon content but showed compensating effects between overestimated growth and underestimated mortality rates.
This first multi\uffe2\uff80\uff90model benchmarking identifies growth and mortality rates as critical calibration targets and highlights the need to refine postdisturbance establishment conditions for model development. We outline specific benchmarking variables needed to improve predictions of forest responses to environmental change.