Comparing surface-soil moisture from the SMOS mission and the ORCHIDEE land-surface model over the Iberian Peninsula

The aim of this study is to compare the surface soil moisture (SSM) retrieved from ESA's Soil Moisture and Ocean Salinity mission (SMOS) with the output of the ORCHIDEE (ORganising Carbon and Hydrology In Dynamic EcosystEm) land surface model forced with two distinct atmospheric data sets for the period 2010 to 2012. The comparison methodology is first established over the REMEDHUS (Red de Estaciones de MEDición de la Humedad def Suelo) soil moisture measurement network, a 30 by 40. km catchment located in the central part of the Duero basin, then extended to the whole Iberian Peninsula (IP). The temporal correlation between the in-situ, remotely sensed and modelled SSM are satisfactory (r. >. 0.8). The correlation between remotely sensed and modelled SSM also holds when computed over the IP. Still, by using spectral analysis techniques, important disagreements in the effective inertia of the corresponding moisture reservoir are found. This is reflected in the spatial correlation over the IP between SMOS and ORCHIDEE SSM estimates, which is poor (¿. ~. 0.3). A single value decomposition (SVD) analysis of rainfall and SSM shows that the co-varying patterns of these variables are in reasonable agreement between both products. Moreover the first three SVD soil moisture patterns explain over 80% of the SSM variance simulated by the model while the explained fraction is only 52% of the remotely sensed values. These results suggest that the rainfall-driven soil moisture variability may not account for the poor spatial correlation between SMOS and ORCHIDEE products.Peer ReviewedPostprint (published version

Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

L-band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture (SSM) by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm which yields SSM estimates. The work exposed compares brightness temperatures measured by the SMOS mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The two modelled sets were estimated using a radiative transfer model and state variables from two land-surface models: (i) ORCHIDEE and (ii) H-TESSEL. The radiative transfer model used is the CMEM. Measured and modelled brightness temperatures show a good agreement in their temporal evolution, but their spatial structures are not consistent. An empirical orthogonal function analysis of the brightness temperature's error identifies a dominant structure over the south-west of the Iberian Peninsula which evolves during the year and is maximum in autumn and winter. Hypotheses concerning forcing-induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for the weak spatial correlations at the moment. Further hypotheses are proposed and will be explored in a forthcoming paper. The analysis of spatial inconsistencies between modelled and measured TBs is important, as these can affect the estimation of geophysical variables and TB assimilation in operational models, as well as result in misleading validation studies

Biogenic isoprene emissions, dry deposition velocity, and surface ozone concentration during summer droughts, heatwaves, and normal conditions in southwestern Europe

At high concentrations, tropospheric ozone (O3) deteriorates air quality, inducing adverse effects on human and ecosystem health. Meteorological conditions are key to understanding the variability in O3 concentration, especially during extreme weather events. In addition to modifying photochemistry and atmospheric transport, droughts and heatwaves affect the state of vegetation and thus the biosphere–troposphere interactions that control atmospheric chemistry, namely biogenic emissions of precursors and gas dry deposition. A major source of uncertainty and inaccuracy in the simulation of surface O3 during droughts and heatwaves is the poor representation of such interactions. This publication aims at quantifying the isolated and combined impacts of both extremes on biogenic isoprene (C5H8) emissions, O3 dry deposition, and surface O3 in southwestern Europe. First, the sensitivity of biogenic C5H8 emissions, O3 dry deposition, and surface O3 to two specific effects of droughts, the decrease in soil moisture and in biomass, is analysed for the extremely dry summer 2012 using the biogenic emission model MEGANv2.1 and the chemistry transport model CHIMEREv2020r1. Despite a significant decrease in biogenic C5H8 emissions and O3 dry deposition velocity, characterized by a large spatial variability, the combined effect on surface O3 concentration remains limited (between +0.5 % and +3 % over the continent). The variations in simulated biogenic C5H8 emissions, O3 dry deposition, and surface O3 during the heatwaves and agricultural droughts are then analysed for summer 2012 (warm and dry), 2013 (warm), and 2014 (relatively wet and cool). We compare the results with large observational data sets, namely O3 concentrations from Air Quality (AQ) e-Reporting (2000–2016) and total columns of formaldehyde (HCHO, which is used as a proxy for biogenic emissions of volatile organic compounds) from the Ozone Monitoring Instrument (OMI) of the Aura satellite (2005–2016). Based on a cluster approach using the percentile limit anomalies indicator, we find that C5H8 emissions increase by +33 % during heatwaves compared to normal conditions, do not vary significantly during all droughts (either accompanied or not by a heatwave), and decrease by −16 % during isolated droughts. OMI data confirm an average increase in HCHO during heatwaves (between +15 % and +31 % depending on the product used) and decrease in HCHO (between −2 % and −6 %) during isolated droughts over the 2005–2016 summers. Simulated O3 dry deposition velocity decreases by −25 % during heatwaves and −35 % during all droughts. Simulated O3 concentrations increase by +7 % during heatwaves and by +3 % during all droughts. Compared to observations, CHIMERE tends to underestimate the daily maximum O3. However, similar sensitivity to droughts and heatwaves are obtained. The analysis of the AQ e-Reporting data set shows an average increase of +14 % during heatwaves and +7 % during all droughts over the 2000–2016 summers (for an average daily concentration value of 69 µg m−3 under normal conditions). This suggests that identifying the presence of combined heatwaves is fundamental to the study of droughts on surface–atmosphere interactions and O3 concentration.</p

UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python

The land surface, hydrological, and groundwater modelling communities all have expertise in simulating the hydrological processes at play in the terrestrial component of the Earth system. However, these communities, and the wider Earth system modelling community, have largely remained distinct with limited collaboration between disciplines, hindering progress in the representation of hydrological processes in the land component of Earth system models (ESMs). In order to address key societal questions regarding the future availability of water resources and the intensity of extreme events such as floods and droughts in a changing climate, these communities must come together and build on the strengths of one another to produce next-generation land system models that are able to adequately simulate the terrestrial water cycle under change. The development of a common modelling infrastructure can contribute to stimulating cross-fertilisation by structuring and standardising the interactions. This paper presents such an infrastructure, a land system framework, which targets an intermediate level of complexity and constrains interfaces between components (and communities) and, in doing so, aims to facilitate an easier pipeline between the development of (sub-)community models and their integration, both for standalone use and for use in ESMs. This paper first outlines the conceptual design and technical capabilities of the framework; thereafter, its usage and useful characteristics are demonstrated through case studies. The main innovations presented here are (1) the interfacing constraints themselves; (2) the implementation in Python (the Unified Framework for Hydrology, unifhy); and (3) the demonstration of standalone use cases using the framework. The existing framework does not yet meet all our goals, in particular, of directly supporting integration into larger ESMs, so we conclude with the remaining limitations of the current framework and necessary future developments.</p

AMMA information system: an efficient cross-disciplinary tool and a legacy for forthcoming projects

International audienceIn the framework of the African Monsoon Multidisciplinary Analyses (AMMA) programme, several tools have been developed in order to facilitate and speed up data and information exchange between researchers from different disciplines. The AMMA information system includes a multidisciplinary user-friendly distributed data management and distribution system, a reports and quick looks archive associated with a display website and scientific papers exchange systems. All the applications have been developed by several French institutions and fully duplicated in Niamey, Niger

Hydro-JULES system design

The Hydro-JULES programme is supported under NERC National Capability and delivered by UKCEH in partnership with NCAS and BGS. The purpose of this document is to describe the Hydro-JULES modelling framework, which has been informed by several stakeholder consultations undertaken during the requirements-gathering phase of the programme

Assessment of precipitation error propagation in multi-model global water resource reanalysis

This study focuses on the Iberian Peninsula and investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modeling, in global water resource reanalysis. Analysis is based on ensemble hydrologic simulations for a period spanning 11 years (2000–2010). To simulate the hydrological variables of surface runoff, subsurface runoff, and evapotranspiration, we used four land surface models (LSMs) – JULES (Joint UK Land Environment Simulator), ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems), SURFEX (Surface Externalisée), and HTESSEL (Hydrology – Tiled European Centre for Medium-Range Weather Forecasts – ECMWF – Scheme for Surface Exchanges over Land) – and one global hydrological model, WaterGAP3 (Water – a Global Assessment and Prognosis). Simulations were carried out for five precipitation products – CMORPH (the Climate Prediction Center Morphing technique of the National Oceanic and Atmospheric Administration, or NOAA), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), 3B42V(7), ECMWF reanalysis, and a machine-learning-based blended product. As a reference, we used a ground-based observation-driven precipitation dataset, named SAFRAN, available at 5 km, 1 h resolution. We present relative performances of hydrologic variables for the different multi-model and multi-forcing scenarios. Overall, results reveal the complexity of the interaction between precipitation characteristics and different modeling schemes and show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure. Surface runoff is strongly sensitive to precipitation uncertainty, and the degree of sensitivity depends significantly on the runoff generation scheme of each model examined. Evapotranspiration fluxes are comparatively less sensitive for this study region. Finally, our results suggest that there is no single model–forcing combination that can outperform all others consistently for all variables examined and thus reinforce the fact that there are significant benefits to exploring different model structures as part of the overall modeling approaches used for water resource applications

The First 30 years of GEWEX

International audienceAbstract The Global Energy and Water Cycle EXchanges (GEWEX) project was created more than thirty years ago within the framework of the World Climate Research Programme (WCRP). The aim of this initiative was to address major gaps in our understanding of Earth’s energy and water cycles given a lack of information about the basic fluxes and associated reservoirs of these cycles. GEWEX sought to acquire and set standards for climatological data on variables essential for quantifying water and energy fluxes and for closing budgets at the regional and global scales. In so doing, GEWEX activities led to a greatly improved understanding of processes and our ability to predict them. Such understanding was viewed then, as it remains today, essential for advancing weather and climate prediction from global to regional scales. GEWEX has also demonstrated over time the importance of a wider engagement of different communities and the necessity of international collaboration for making progress on understanding and on the monitoring of the changes in the energy and water cycles under ever increasing human pressures. This paper reflects on the first 30 years of evolution and progress that has occurred within GEWEX. This evolution is presented in terms of three main phases of activity. Progress toward the main goals of GEWEX is highlighted by calling out a few achievements from each phase. A vision of the path forward for the coming decade, including the goals of GEWEX for the future, are also described

Evaluating the performance of land surface model ORCHIDEE-CAN v1.0 on water and energy flux estimation with a single- and multi-layer energy budget scheme

Canopy structure is one of the most important vegetation characteristics for land-atmosphere interactions, as it determines the energy and scalar exchanges between the land surface and the overlying air mass. In this study we evaluated the performance of a newly developed multilayer energy budget in the ORCHIDEE-CAN v1.0 land surface model (Organising Carbon and Hydrology In Dynamic Ecosystems - CANopy), which simulates canopy structure and can be coupled to an atmospheric model using an implicit coupling procedure. We aim to provide a set of accept-able parameter values for a range of forest types. Top-canopy and sub-canopy flux observations from eight sites were collected in order to conduct this evaluation. The sites crossed climate zones from temperate to boreal and the vegetation types included deciduous, evergreen broad-leaved and evergreen needle-leaved forest with a maximum leaf area index (LAI; all-sided) ranging from 3.5 to 7.0. The parametrization approach proposed in this study was based on three selected physical processes - namely the diffusion, advection, and turbulent mixing within the canopy. Short-term sub-canopy observations and long-term surface fluxes were used to calibrate the parameters in the sub-canopy radiation, turbulence, and resistance modules with an automatic tuning process. The multi-layer model was found to capture the dynamics of sub-canopy turbulence, temperature, and energy fluxes. The performance of the new multi-layer model was further compared against the existing single-layer model. Although the multi-layer model simulation results showed few or no improvements to both the nighttime energy balance and energy partitioning during winter compared with a single-layer model simulation, the increased model complexity does provide a more detailed description of the canopy micrometeorology of various forest types. The multi-layer model links to potential future environmental and ecological studies such as the assessment of in-canopy species vulnerability to climate change, the climate effects of disturbance intensities and frequencies, and the consequences of biogenic volatile organic compound (BVOC) emissions from the terrestrial ecosystem.Peer reviewe

Advancing research for seamless Earth system prediction

Whether on an urban or planetary scale, covering time scales of a few minutes or a few decades, the societal need for more accurate weather, climate, water, and environmental information has led to a more seamless thinking across disciplines and communities. This challenge, at the intersection of scientific research and society’s need, is among the most important scientific and technological challenges of our time. The “Science Summit on Seamless Research for Weather, Climate, Water, and Environment” organized by the World Meteorological Organization (WMO) in 2017, has brought together researchers from a variety of institutions for a cross-disciplinary exchange of knowledge and ideas relating to seamless Earth system science. The outcomes of the Science Summit, and the interactions it sparked, highlight the benefit of a seamless Earth system science approach. Such an approach has the potential to break down artificial barriers that may exist due to different observing systems, models, time and space scales, and compartments of the Earth system. In this context, the main future challenges for research infrastructures have been identified. A value cycle approach has been proposed to guide innovation in seamless Earth system prediction. The engagement of researchers, users, and stakeholders will be crucial for the successful development of a seamless Earth system science that meets the needs of society