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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.

The Copernicus Atmosphere Monitoring Service (CAMS) provides daily analyses and forecasts of the composition of the atmosphere, including the reactive gases such as O3 , CO , NO2 , HCHO and SO2 ; aerosol species; and greenhouse gases. The global CAMS analysis system (IFS-COMPO) is based on the ECMWF Integrated Forecasting System (IFS) for numerical weather prediction (NWP) and assimilates a large number of composition satellite products on top of the meteorological observations ingested in IFS. The CAMS system receives regular upgrades, following the upgrades of IFS. The last upgrade, Cy48R1, operational since 27 June 2023, was major with a large number of code changes, both for IFS-COMPO and for NWP. The main IFS-COMPO innovations include the introduction of full stratospheric chemistry; a major update of the emissions; a major update of the aerosol model, including the representation of secondary organic aerosol; several updates of the dust life cycle and optics; updates to the inorganic chemistry in the troposphere; and the assimilation of Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol optical depth (AOD) and TROPOspheric Monitoring Instrument (TROPOMI) CO. The CAMS Cy48R1 upgrade was validated using a large number of independent measurement datasets, including surface in situ, surface remote sensing, routine aircraft, and balloon and satellite observations. In this paper we present the validation results for Cy48R1 by comparing them with the skill of the previous operational system (Cy47R3), with the independent observations as reference, for the period October 2022 to June 2023, during which daily forecasts from both cycles are available. Major improvements in skill are found for the ozone profile in the lower–middle stratosphere and for stratospheric NO2 due to the inclusion of full stratospheric chemistry. Stratospheric trace gases compare well with the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) observations between 10 and 200 hPa, with larger deviations between 1 and 10 hPa. The impact of the updated emissions is especially visible over East Asia and is beneficial for the trace gases O3 , NO2 and SO2. The CO column assimilation is now anchored by the Infrared Atmospheric Sounding Interferometer (IASI) instead of the Measurements Of Pollution in The Troposphere (MOPITT) instrument, which is beneficial for most of the CO comparisons, and the assimilation of TROPOMI CO data improves the model CO field in the troposphere. In general the aerosol optical depth has improved globally, but the dust evaluation shows more mixed results. The results of the 47 comparisons are summarised in a scorecard, which shows that 83 % of the evaluation datasets show a neutral or improved performance of Cy48R1 compared to the previous operational CAMS system, while 17 % indicate a (slight) degradation. This demonstrates the overall success of this upgrade. [ABSTRACT FROM AUTHOR]

Global monitoring of aerosols is required to analyse the impacts of aerosols on air quality and to understand their role in modulating the climate variability. The Copernicus Atmosphere Monitoring Service (CAMS) provides near-real-time forecasts and reanalyses of aerosols using the ECMWF Integrated Forecasting System (IFS), constrained by the assimilation of MODIS and Polar Multi-Sensor Aerosol Optical Properties (PMAp) aerosol optical depth (AOD). Given the potential end of lifetime of MODIS AOD, implementing new AOD observations in the CAMS operational suite is a priority to ensure the continuity of the CAMS forecast performances. The objective of this work is to test the assimilation of the NOAA VIIRS AOD product from S-NPP and NOAA20 satellites in the IFS model. Simulation experiments assimilating VIIRS on top or in place of MODIS were carried out from June 2021 to November 2021 to evaluate the impacts on the AOD analysis. For maritime aerosol background, the assimilation of VIIRS and the use of VIIRS from NOAA20 as an anchor reduce the analysis AOD values compared to MODIS-based experiments, in which the analysis values were too high due to the positive bias of MODIS/Terra over ocean. Over land, the assimilation of VIIRS induces a large increase in the analysis over biomass burning regions where VIIRS shows larger AOD than MODIS due to differences in the aerosol models and cloud filtering between MODIS and VIIRS retrieval algorithms. For dust source regions, the analysis is reduced when VIIRS is assimilated on top of or in place of MODIS, particularly over the Sahara, the Arabian Peninsula and a few places in Asia in the July–August period. The assimilation of VIIRS leads to an overall reduction of the bias in AOD analysis evaluated against AERONET measurements, with the largest bias reduction over Europe and desert and maritime sites. [ABSTRACT FROM AUTHOR]

The Copernicus Atmosphere Monitoring Service (CAMS) provides near-real-time forecast and reanalysis of aerosols using the ECMWF Integrated Forecasting System with atmospheric composition extension, constrained by the assimilation of MODIS and the Polar Multi-Sensor Aerosol Optical Properties (PMAp) aerosol optical depth (AOD). The objective of this work is to evaluate two new near-real-time AOD products to prepare for their assimilation into CAMS, namely the Copernicus AOD (collection 1) from the Sea and Land Surface Temperature Radiometer (SLSTR) on board Sentinel 3-A/B over ocean and the NOAA EPS AOD (v2.r1) from VIIRS on board S-NPP and NOAA20 over both land and ocean. The differences between MODIS (C6.1), PMAp (v2.1), VIIRS (v2.r1), and SLSTR (C1) AOD as well as their departure from the modeled AOD were assessed at the model grid resolution (i.e., level-3) using the 3-month AOD average (December 2019–February 2020 and March–May 2020). VIIRS and MODIS show the best consistency across the products, which is explained by instrument and retrieval algorithm similarities. VIIRS AOD is frequently lower over the ocean background and higher over biomass burning and dust source land regions compared to MODIS. VIIRS shows larger spatial coverage over land and resolves finer spatial structures such as the transport of Australian biomass burning smoke over the Pacific, which can be explained by the use of a heavy aerosol detection test in the retrieval algorithm. Our results confirm the positive offset over ocean (i) between Terra/MODIS and Aqua/MODIS due to the non-corrected radiometric calibration degradation of Terra/MODIS in the Dark Target algorithm and (ii) between SNPP/VIIRS and NOAA20/VIIRS due to the positive bias in the solar reflective bands of SNPP/VIIRS. SLSTR AOD shows much smaller level-3 values than the rest of the products, which is mainly related to differences in spatial representativity at the IFS grid spatial resolution due to the stringent cloud filtering applied to the SLSTR radiances. Finally, the geometry characteristics of the instrument, which drive the range of scattering angles sampled by the instrument, can explain a large part of the differences between retrievals such as the positive offset between PMAp datasets from MetOp-B and MetOp-A. [ABSTRACT FROM AUTHOR]

The spatial structures displayed by remote sensing imagery are essential information characterizing the nature and the scale of spatial variation of Earth surface processes. This paper provides a new approach to characterize the spatial structures within remote sensing imagery using stochastic models and geostatistic metrics. Up to now, the second-order variogram has been widely used to describe the spatial variations within an image. In this paper, we demonstrate its limitation to discriminate distinct image spatial structures. We introduce a different geostatistic metric, the first-order variogram, which used in combination with the second-order variogram, will prove its efficiency to describe the image spatial structures. We then develop a method based on the simultaneous use of both first- and second-order variogram metrics to model the image spatial structures as the weighted linear combination of two stochastic models: a Poisson line mosaic model and a multi-Gaussian model. The image spatial structures are characterized by the variance weight and the variogram range related to each model. This method is applied to several SPOT-HRV Normalized Difference Vegetation Index (NDVI) images from the VALERI database in order to characterize the nature of the processes structuring different types of landscape. The mosaic model is an indicator of strong NDVI discontinuities within the image mainly generated by anthropogenic processes such as the mosaic pattern of crop sites. The multi-Gaussian model shows evidence of diffuse and continuous variation of NDVI generally engendered by ecological and environmental processes such as the fuzzy pattern observed over forest and natural vegetation sites. Index Terms--First-order variogram, landscape, multi-Gaussian model, normalized difference vegetation index (NDVI), Poisson line mosaic model, second-order variogram, spatial structure, stochastic simulation.

Initiated in 1984, the Committee Earth Observing Satellites' Working Group on Calibration and Validation (CEOS WGCV) pursues activities to coordinate, standardize and advance calibration and validation of civilian satellites and their data. One subgroup of CEOS WGCV, Land Product Validation (LPV), was established in 2000 to define standard validation guidelines and protocols and to foster data and information exchange relevant to the validation of land products. Since then, a number of leaf area index (LAI) products have become available to the science community at both global and regional extents. Having multiple global LAI products and multiple, disparate validation activities related to these products presents the opportunity to realize efficiency through international collaboration. So the LPV subgroup established an international LAI intercomparison validation activity. This paper describes the main components of this international validation effort. The paper documents the current participants, their ground LAI measurements and scaling techniques, and the metadata and infrastructure established to share data. The paper concludes by describing plans for sharing both field data and high-resolution LAI products from each site. Many considerations of this global LAI intercomparison can apply to other products, and this paper presents a framework for such collaboration. Index Terms--Committee on Earth Observing Satellites (CEOS) leaf area index (LAI), validation.

This study investigates the representativeness of land cover and leaf area index (LAI) sampled by a global network of sites to be used for the evaluation of land biophysical products, such as LAI or fAPAR, derived from current satellite systems. The networks of sites considered include 100 sites where ground measurements of LAI or fAPAR have been performed for the validation of medium resolution satellite land biophysical products, 188 FLUXNET sites and 52 AERONET sites. All the sites retained had less than 25% of water bodies within a 8 x 8 [km.sup.2] window, and were separated by more than 20 km. The ECOCLIMAP global classification was used to quantify the representativeness of the networks. It allowed describing the Earth's surface with seven main types and proposed a climatology for monthly LAI values at a spatial resolution around 1 km. The site distribution indicates a large over representation of the northern midlatitudes relative to other regions, and an under-representation of bare surfaces, grass, and evergreen broadleaf forests. These three networks represent all together 295 sites after elimination of sites that were too close. They were thus completed by 76 additional sites to improve the representativeness in latitude, longitude, and surface type. This constitutes the BELMANIP network proposed as a benchmark for intercomparison of land biophysical products. Suitable approaches to conducting intercomparison at the sites are recommended. Index Terms--Global land biophysical products, intercomparison, leaf area index (LAI), validation.

Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the "soil14_psi" experiments), when the critical threshold value for inducing soil moisture stress was reduced ("soil14_p0"), and when plants were able to access soil moisture in deeper soil layers ("soil14_dr*2"). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased model biases but improved the simulated seasonal cycle and brought the monthly variance closer to the measured variance of LE. Further evaluation of the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES or as a general way to improve land surface carbon and water fluxes in other models. In addition, using soil matric potential presents the opportunity to include plant functional type-specific parameters to further improve modeled fluxes. [ABSTRACT FROM AUTHOR]

Drought is predicted to increase in the future due to climate change, bringing with it a myriad of impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance, in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local/regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales, and evaluated ten different representations of stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high latitudes/cold region sites, while LE was best simulated in temperate and high latitude/cold sites. Errors not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savannah and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14, and the soil depth from 3m to 10.8m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation, when the onset of stress was delayed, and when roots extended deeper into the soil. For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and made the simulation worse. Further evaluation into the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress. [ABSTRACT FROM AUTHOR]

Global NDVI data are routinely derived from the AVHRR, SPOT-VGT, and MODIS/Terra earth observation records for a range of applications from terrestrial vegetation monitoring to climate change modeling. This has led to a substantial interest in the harmonization of multisensor records. Most evaluations of the internal consistency and continuity of global multisensor NDVI products have focused on time-series harmonization in the spectral domain, often neglecting the spatial domain. We fill this void by applying variogram modeling (a) to evaluate the differences in spatial variability between 8-km AVHRR, 1-km SPOT-VGT, and 1-km, 500-m, and 250-m MODIS NDVI products over eight EOS (Earth Observing System) validation sites, and (b) to characterize the decay of spatial variability as a function of pixel size (i.e. data regularization) for spatially aggregated Landsat ETM+ NDVI products and a real multisensor dataset. First, we demonstrate that the conjunctive analysis of two variogram properties – the sill and the mean length scale metric – provides a robust assessment of the differences in spatial variability between multiscale NDVI products that are due to spatial (nominal pixel size, point spread function, and view angle) and non-spatial (sensor calibration, cloud clearing, atmospheric corrections, and length of multi-day compositing period) factors. Next, we show that as the nominal pixel size increases, the decay of spatial information content follows a logarithmic relationship with stronger fit value for the spatially aggregated NDVI products (R 2 =0.9321) than for the native-resolution AVHRR, SPOT-VGT, and MODIS NDVI products (R 2 =0.5064). This relationship serves as a reference for evaluation of the differences in spatial variability and length scales in multiscale datasets at native or aggregated spatial resolutions. The outcomes of this study suggest that multisensor NDVI records cannot be integrated into a long-term data record without proper consideration of all factors affecting their spatial consistency. Hence, we propose an approach for selecting the spatial resolution, at which differences in spatial variability between NDVI products from multiple sensors are minimized. This approach provides practical guidance for the harmonization of long-term multisensor datasets. [Copyright &y& Elsevier]

Enhanced modelling of evapotranspiration (ET) is required to address important research questions such as the impacts of changes in land-cover and land-use on the hydrological response of catchments to rainfall. ET has been recognized as one of the most uncertain terms in the surface water balance simulated by land surface models. Uncertainties in simulated ET can be attributed to (i) model structure and parameters and (ii) errors in the forcing variables used to drive the model and to integrate it spatially. A single-source surface energy balance does not account for radiative and turbulent interactions between the canopy air space and the substrate below the canopy. This can be a strong source of structural uncertainties in surface fluxes simulated over heterogeneous surfaces (e.g. forest canopy partially buried by snow, sparse vegetation with large fraction of bare ground) for which the substrate has a significant impact on the total energy balance. However, while separate representation of the energy exchanges for the substrate and the canopy should provide more realistic modelling framework, the larger number of parameters may increase the total uncertainties for large-scale simulations. This goal of this work is to assess the uncertainties in the modelling of the energy balance in a land surface model and its impact on the simulation of evapotranspiration and the partitioning between soil evaporation, plant transpiration and canopy interception. This study was achieved within the Hydro-JULES project which aims at developing an integrated land surface-hydrological modelling platform in conjunction with the Joint UK Land Environment Simulator (JULES) land surface model. We evaluated the dual-source scheme that has been recently implemented in the JULES land surface model. This work has two objectives:- assess the performances of the JULES dual-source against in situ observations at various contrasted sites: BERMS (Saskatchewan) forest site; Avignon (France) Mediterranean crop site and semi-arid olive orchard sites in Morocco and Tunisia. - conduct a sensitivity and uncertainty analysis to identify the most influential parameters of the dual-source scheme on the simulation of evapotranspiration and its partitioning.The outcomes of this work will help to identify in which environmental conditions (soil, climate, surface heterogeneity) a dual-source scheme provides more accurate estimation of total evapotranspiration than a single-source model. The partitioning of ET into soil evaporation and plant transpiration will also be evaluated for sites for which sap flow or isotopic measurements were available. This study will bring key insights on the compromise to reach between enhanced modelling complexity and increased uncertainties generated by a larger number of parameters. [ABSTRACT FROM AUTHOR]