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Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles in both future climate projections and weather forecasts. Evaluation of models' ability to do this is performed in a wide range of simulation environments, often without explicit consideration of the degree of observational constraint or uncertainty and typically without quantification of benchmark performance expectations. We describe a Model Intercomparison Project (MIP) that attempts to resolve these shortcomings, comparing the surface turbulent heat flux predictions of around 20 different land models provided with in situ meteorological forcing evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance-based flux tower sites. Predictions from seven out-of-sample empirical models are used to quantify the information available to land models in their forcing data and so the potential for land model performance improvement. Sites with unusual behaviour, complicated processes, poor data quality, or uncommon flux magnitude are more difficult to predict for both mechanistic and empirical models, providing a means of fairer assessment of land model performance. When examining observational uncertainty, model performance does not appear to improve in low-turbulence periods or with energy-balance-corrected flux tower data, and indeed some results raise questions about whether the energy balance correction process itself is appropriate. In all cases the results are broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models. In all but two cases, latent heat flux and net ecosystem exchange of CO2 are better predicted by land models than sensible heat flux, despite it seeming to have fewer physical controlling processes. Land models that are implemented in Earth system models also appear to perform notably better than stand-alone ecosystem (including demographic) models, at least in terms of the fluxes examined here. The approach we outline enables isolation of the locations and conditions under which model developers can know that a land model can improve, allowing information pathways and discrete parameterisations in models to be identified and targeted for future model development.

The Tibetan Plateau (TP) contains the largest amount of snow outside the polar regions and is the source of many major rivers in Asia. An accurate long-range (i.e. seasonal) meteorological forecast is of great importance for this region. The fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5) provides global long-range meteorological forecasts including over the TP. However, SEAS5 uses land initial conditions produced by assimilating Interactive Multisensor Snow and Ice Mapping System (IMS) snow data only below 1500 m altitude, which may affect the forecast skill of SEAS5 over mountainous regions like the TP. To investigate the impacts of snow assimilation on the forecasts of snow, temperature and precipitation, twin ensemble reforecasts are initialized with and without snow assimilation above 1500 m altitude over the TP for spring and summer 2018. Significant changes occur in the springtime. Without snow assimilation, the reforecasts overestimate snow cover and snow depth while underestimating daily temperature over the TP. Compared to satellite-based estimates, precipitation reforecasts perform better in the west TP (WTP) than in the east TP (ETP). With snow assimilation, the reforecasts of snow cover, snow depth and temperature are consistently improved in the TP in the spring. However, the positive bias between the precipitation reforecasts and satellite observations worsens in the ETP. Compared to the experiment with no snow assimilation, the snow assimilation experiment significantly increases temperature and precipitation for the ETP and around the longitude 95∘ E. The higher temperature after snow assimilation, in particular the cold bias reduction after initialization, can be attributed to the effects of a more realistic, decreased snowpack, providing favourable conditions for generating more precipitation. Overall, snow assimilation can improve seasonal forecasts through the interaction between land and atmosphere.

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.

Nowadays many seasonal forecasting centres provide dynamical predictions of sea ice. While initializing sea ice by assimilating sea ice concentration (SIC) is common, constraining initial conditions of sea ice thickness (SIT) is only in its early stages. Here, we make use of the availability of Arctic-wide winter SIT observations covering 2011–2016 to constrain SIT in the ECMWF (European Centre for Medium-Range Weather Forecasts) ocean–sea-ice analysis system with the aim of improving the initial conditions of the coupled forecasts. The impact of the improved initialization on the predictive skill of pan-Arctic sea ice for lead times of up to 7 months is investigated in a low-resolution analogue of the currently operational ECMWF seasonal forecasting system SEAS5. By using winter SIT information merged from CS2 and SMOS (CS2SMOS: CryoSat-2 Soil Moisture and Ocean Salinity), substantial changes in sea ice volume and thickness are found in the ocean–sea-ice analysis, including damping of the overly strong seasonal cycle of sea ice volume. Compared with the reference experiment, which does not use SIT information, forecasts initialized using SIT data show a reduction of the excess sea ice bias and an overall reduction of seasonal sea ice area forecast errors of up to 5 % at lead months 2 to 5. Change in biases is the main forecast impact. Using the integrated ice edge error (IIEE) metric, we find significant improvement of up to 28 % in the September sea ice edge forecast started in April. However, sea ice forecasts for September started in spring still exhibit a positive sea ice bias, which points to a melting that is too slow in the forecast model. A slight degradation in skill is found in the early freezing season sea ice forecasts initialized in July and August, which is related to degraded initial conditions during these months. Both ocean reanalyses, with and without SIT constraint, show strong melting in the middle of the melt season compared to the forecasts. This excessive melting related to positive net surface radiation biases in the atmospheric flux forcing of the ocean reanalyses remains and consequently degrades analysed summer SIC. The impact of thickness initialization is also visible in the sea surface and near-surface temperature forecasts. While positive forecast impact is seen in near-surface temperature forecasts of early freezing season (September–October–November) initialized in May (when the sea ice initial conditions have been observationally constrained in the preceding winter months), negative impact is seen for the same season when initialized in the month of August when the sea ice initial conditions are degraded. We conclude that the strong thinning by CS2SMOS initialization mitigates or enhances seasonally dependent forecast model errors in sea ice and near-surface temperatures in all seasons. The results indicate that the memory of SIT in the spring initial conditions lasts into autumn, influencing forecasts of the peak summer melt and early freezing seasons. Our results demonstrate the usefulness of new sea ice observational products in both data assimilation and forecasting systems, and they strongly suggest that better initialization of SIT is crucial for improving seasonal sea ice forecasts.

LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model (LSM). This study demonstrates that LDAS-Monde is able to detect, monitor and forecast the impact of extreme weather on land surface states. Firstly, LDAS-Monde is run globally at 0.25∘ spatial resolution over 2010–2018. It is forced by the state-of-the-art ERA5 reanalysis (LDAS_ERA5) from the European Centre for Medium Range Weather Forecasts (ECMWF). The behaviour of the assimilation system is evaluated by comparing the analysis with the assimilated observations. Then the land surface variables (LSVs) are validated with independent satellite datasets of evapotranspiration, gross primary production, sun-induced fluorescence and snow cover. Furthermore, in situ measurements of SSM, evapotranspiration and river discharge are employed for the validation. Secondly, the global analysis is used to (i) detect regions exposed to extreme weather such as droughts and heatwave events and (ii) address specific monitoring and forecasting requirements of LSVs for those regions. This is performed by computing anomalies of the land surface states. They display strong negative values for LAI and SSM in 2018 for two regions: north-western Europe and the Murray–Darling basin in south-eastern Australia. For those regions, LDAS-Monde is forced with the ECMWF Integrated Forecasting System (IFS) high-resolution operational analysis (LDAS_HRES, 0.10∘ spatial resolution) over 2017–2018. Monitoring capacities are studied by comparing open-loop and analysis experiments, again against the assimilated observations. Forecasting abilities are assessed by initializing 4 and 8 d LDAS_HRES forecasts of the LSVs with the LDAS_HRES assimilation run compared to the open-loop experiment. The positive impact of initialization from an analysis in forecast mode is particularly visible for LAI that evolves at a slower pace than SSM and is more sensitive to initial conditions than to atmospheric forcing, even at an 8 d lead time. This highlights the impact of initial conditions on LSV forecasts and the value of jointly analysing soil moisture and vegetation states.

The Tibetan Plateau (TP) region, often referred to as the Third Pole, is the world's highest plateau and exerts a considerable influence on regional and global climate. The state of the snowpack over the TP is a major research focus due to its great impact on the headwaters of a dozen major Asian rivers. While many studies have attempted to validate atmospheric reanalyses over the TP area in terms of temperature or precipitation, there have been – remarkably – no studies aimed at systematically comparing the snow depth or snow cover in global reanalyses with satellite and in situ data. Yet, snow in reanalyses provides critical surface information for forecast systems from the medium to sub-seasonal timescales. Here, snow depth and snow cover from four recent global reanalysis products, namely the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 and ERA-Interim reanalyses, the Japanese 55-year Reanalysis (JRA-55) and the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-2), are inter-compared over the TP region. The reanalyses are evaluated against a set of 33 in situ station observations, as well as against the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and a satellite microwave snow depth dataset. The high temporal correlation coefficient (0.78) between the IMS snow cover and the in situ observations provides confidence in the station data despite the relative paucity of in situ measurement sites and the harsh operating conditions. While several reanalyses show a systematic overestimation of the snow depth or snow cover, the reanalyses that assimilate local in situ observations or IMS snow cover are better capable of representing the shallow, transient snowpack over the TP region. The latter point is clearly demonstrated by examining the family of reanalyses from the ECMWF, of which only the older ERA-Interim assimilated IMS snow cover at high altitudes, while ERA5 did not consider IMS snow cover for high altitudes. We further tested the sensitivity of the ERA5-Land model in offline experiments, assessing the impact of blown snow sublimation, snow cover to snow depth conversion and, more importantly, excessive snowfall. These results suggest that excessive snowfall might be the primary factor for the large overestimation of snow depth and cover in ERA5 reanalysis. Pending a solution for this common model precipitation bias over the Himalayas and the TP, future snow reanalyses that optimally combine the use of satellite snow cover and in situ snow depth observations in the assimilation and analysis cycles have the potential to improve medium-range to sub-seasonal forecasts for water resources applications.

The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

Measurements of the surface soil moisture (SM) content are important for a wide range of applications. Among them, operational hydrology and numerical weather prediction, for instance, need SM information in near-real-time (NRT), typically not later than 3 h after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure SM from space. The ESA Level 2 SM retrieval algorithm is based on a detailed geophysical modelling and cannot provide SM in NRT. This paper presents the new ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in NRT. The NN inputs are SMOS brightness temperatures for horizontal and vertical polarizations and incidence angles from 30 to 45°. In addition, the NN uses surface soil temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The NN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is somewhat narrower (∼ 915 km) than that of the L2 SM dataset (∼ 1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data show a standard deviation of the difference with respect to the L2 data of 3 m−3 in most of the Earth and a Pearson correlation coefficient higher than 0.7 in large regions of the globe. The NRT SM dataset does not show a global bias with respect to the L2 dataset but can show local biases of up to 0.05 m3 m−3 in absolute value. The two SMOS SM products were evaluated against in situ measurements of SM from more than 120 sites of the SCAN (Soil Climate Analysis Network) and the USCRN (US Climate Reference Network) networks in North America. The NRT dataset obtains similar but slightly better results than the L2 data. In summary, the NN SMOS NRT SM product exhibits performances similar to those of the Level 2 SM product but it has the advantage of being available in less than 3.5 h after sensing, complying with NRT requirements. The new product is processed at ECMWF and it is distributed by ESA and via the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast).

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.

ERA-Interim/Land is a global land surface reanalysis data set covering the period 1979–2010. It describes the evolution of soil moisture, soil temperature and snowpack. ERA-Interim/Land is the result of a single 32-year simulation with the latest ECMWF (European Centre for Medium-Range Weather Forecasts) land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanalysis and precipitation adjustments based on monthly GPCP v2.1 (Global Precipitation Climatology Project). The horizontal resolution is about 80 km and the time frequency is 3-hourly. ERA-Interim/Land includes a number of parameterization improvements in the land surface scheme with respect to the original ERA-Interim data set, which makes it more suitable for climate studies involving land water resources. The quality of ERA-Interim/Land is assessed by comparing with ground-based and remote sensing observations. In particular, estimates of soil moisture, snow depth, surface albedo, turbulent latent and sensible fluxes, and river discharges are verified against a large number of site measurements. ERA-Interim/Land provides a global integrated and coherent estimate of soil moisture and snow water equivalent, which can also be used for the initialization of numerical weather prediction and climate models.

In situ soil moisture data from 122 stations across the United States are used to evaluate the impact of a new bare ground evaporation formulation at ECMWF. In November 2010, the bare ground evaporation used in ECMWF's operational Integrated Forecasting System (IFS) was enhanced by adopting a lower stress threshold than for the vegetation, allowing a higher evaporation. It results in more realistic soil moisture values when compared to in situ data, particularly over dry areas. Use was made of the operational IFS and offline experiments for the evaluation. The latter are based on a fixed version of the IFS and make it possible to assess the impact of a single modification, while the operational analysis is based on a continuous effort to improve the analysis and modelling systems, resulting in frequent updates (a few times a year). Considering the field sites with a fraction of bare ground greater than 0.2, the root mean square difference (RMSD) of soil moisture is shown to decrease from 0.118 m3 m−3 to 0.087 m3 m−3 when using the new formulation in offline experiments, and from 0.110 m3 m−3 to 0.088 m3 m−3 in operations. It also improves correlations. Additionally, the impact of the new formulation on the terrestrial microwave emission at a global scale is investigated. Realistic and dynamically consistent fields of brightness temperature as a function of the land surface conditions are required for the assimilation of the SMOS data. Brightness temperature simulated from surface fields from two offline experiments with the Community Microwave Emission Modelling (CMEM) platform present monthly mean differences up to 7 K. Offline experiments with the new formulation present drier soil moisture, hence simulated brightness temperature with its surface fields are larger. They are also closer to SMOS remotely sensed brightness temperature.

The potentialities of ENVISAT ASAR (Advanced Synthetic Aperture Radar) multi-angle data for mapping surface soil moisture (SSM) in Sahelian rangelands are investigated at medium scale (30 000 km2). The Wide Swath data are selected to take advantage of their high temporal repetitivity (about 8 days at the considered scale) associated to a moderate spatial resolution (150 m). In the continuity of previous studies conducted at a local scale in the same region, SSM maps are here processed over the whole AMMA Gourma mesoscale site at 1 km resolution scale. Overall, the generated maps are found to be in good agreement with field data, EPSAT-SG (Estimation des Pluies par SATellite – Second Generation) rainfall estimates and ERS (European Remote Sensing) Wind Scatterometer (WSC) SSM products. The present study shows that the spatial pattern of SSM can be realistically estimated at a kilometric scale. The resulting SSM maps are expected to provide valuable information for initialisation of land surface models and the estimation of the spatial distribution of radiative fluxes. Particularly, SSM maps could help to desaggregate low-resolution products such as those derived from WSC data.

The SMOSMANIA soil moisture network in Southwestern France is used to evaluate modelled and remotely sensed soil moisture products. The surface soil moisture (SSM) measured in situ at 5 cm permits to evaluate SSM from the SIM operational hydrometeorological model of Météo-France and to perform a cross-evaluation of the normalised SSM estimates derived from coarse-resolution (25 km) active microwave observations from the ASCAT scatterometer instrument (C-band, onboard METOP), issued by EUMETSAT and resampled to the Discrete Global Grid (DGG, 12.5 km gridspacing) by TU-Wien (Vienna University of Technology) over a two year period (2007–2008). A downscaled ASCAT product at one kilometre scale is evaluated as well, together with operational soil moisture products of two meteorological services, namely the ALADIN numerical weather prediction model (NWP) and the Integrated Forecasting System (IFS) analysis of Météo-France and ECMWF, respectively. In addition to the operational SSM analysis of ECMWF, a second analysis using a simplified extended Kalman filter and assimilating the ASCAT SSM estimates is tested. The ECMWF SSM estimates correlate better with the in situ observations than the Météo-France products. This may be due to the higher ability of the multi-layer land surface model used at ECMWF to represent the soil moisture profile. However, the SSM derived from SIM corresponds to a thin soil surface layer and presents good correlations with ASCAT SSM estimates for the very first centimetres of soil. At ECMWF, the use of a new data assimilation technique, which is able to use the ASCAT SSM, improves the SSM and the root-zone soil moisture analyses.

This paper presents a comparison and an evaluation of five soil moisture products based on satellite-based passive and active microwave measurements. Products are evaluated for 2005–2006 against ground measurements obtained from the soil moisture network deployed in Mali (Sahel) in the framework of the African Monsoon Multidisciplinary Analysis project. It is shown that the accuracy of the soil moisture products is sensitive to the retrieval approach as well as to the sensor type (active or passive) and to the signal frequency (from 5.6 GHz to 18.8 GHz). The spatial patterns of surface soil moisture are compared between the different products at meso-scale (14.5° N–17.5° N and 2° W–1° W). A general good consistency between the different satellite soil moisture products is shown in terms of meso-scale spatial distribution, in particular after convective rainfall occurrences. Comparison to ground measurement shows that although soil moisture products obtained from satellite generally over-estimate soil moisture values during the dry season, most of them capture soil moisture temporal variations in good agreement with ground station measurements.

Abstract: The prediction skill for precipitation anomalies in late spring and summer months—a significant component of extreme climate events—has remained stubbornly low for years. This paper presents a new idea that utilizes information on boreal spring land surface temperature/subsurface temperature (LST/SUBT) anomalies over the Tibetan Plateau (TP) to improve prediction of subsequent summer droughts/floods over several regions over the world, East Asia and North America in particular. The work was performed in the framework of the GEWEX/LS4P Phase I (LS4P-I) experiment, which focused on whether the TP LST/SUBT provides an additional source for subseasonal-to-seasonal (S2S) predictability. The summer 2003, when there were severe drought/flood over the southern/northern part of the Yangtze River basin, respectively, has been selected as the focus case. With the newly developed LST/SUBT initialization method, the observed surface temperature anomaly over the TP has been partially produced by the LS4P-I model ensemble mean, and 8 hotspot regions in the world were identified where June precipitation is significantly associated with anomalies of May TP land temperature. Consideration of the TP LST/SUBT effect has produced about 25–50% of observed precipitation anomalies in most hotspot regions. The multiple models have shown more consistency in the hotspot regions along the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train. The mechanisms for the LST/SUBT effect on the 2003 drought over the southern part of the Yangtze River Basin are discussed. For comparison, the global SST effect has also been tested and 6 regions with significant SST effects were identified in the 2003 case, explaining about 25–50% of precipitation anomalies over most of these regions. This study suggests that the TP LST/SUBT effect is a first-order source of S2S precipitation predictability, and hence it is comparable to that of the SST effect. With the completion of the LS4P-I, the LS4P-II has been launched and the LS4P-II protocol is briefly presented.

An original and simple method to map surface soil moisture over large areas has been developed to obtain data with a high temporal and spatial resolution for the study of possible feedback mechanisms between soil moisture and convection in West Africa. A rainfall estimation product based on Meteosat geostationary satellite measurements is first used together with a simple Antecedent Precipitation Index (API) model to produce soil moisture maps at a spatial resolution of 10×10 km2 and a temporal resolution of 30-min. However, given the uncertainty of the satellite-based rainfall estimation product, the resulting soil moisture maps are not sufficiently accurate. For this reason, a technique based on assimilating AMSR-E C-band measurements into a microwave emission model was developed in which the estimated rainfall rates between two successive AMSR-E brightness temperature (TB) measurements are adjusted by multiplying them by a factor between 0 and 7 that minimizes the difference between simulated and observed TBs. Ground-based soil moisture measurements obtained at three sites in Niger, Mali and Benin were used to assess the method which was found to improve the soil moisture estimates on all three sites.

The aim of this article is to test the sensitivity of the West African hydrological cycle to infiltration processes and to river reinfiltration pathways. This is done through sensitivity experiments to both inputs and paramterization settings of the ORCHIDEE Land-Surface Model. The parameterizations to take into account the effects of flat areas, ponds and floodplains on surface infiltration, and the effect of roots and deep-soil compactness on infiltration are first described. The sensitivity analysis to parameterization settings shows that the surface infiltration processes have a stronger impact in the soudano-sahelian region and more generally in semi-arid African regions, whereas the rootzone and deep-soil infiltration also play a role in the guinean and intermediate regions between arid and humid ones. In the equatorial and semi-humid regions, infiltration processes generally play a minor role. The infiltration parameterizations may explain part of the difference between simulated and observed river discharge in semi-arid and intermediate basins. The sensitivity analysis to the Land-Surface Model inputs shows that different sources of uncertainty might also explain part of the error. Indeed, the precipitation forcing in the whole West African region, the long-term storage in the soudano-sahelian region, the soil types in the guinean region and the vegetation types in the equatorial region are significant sources of errors. Therefore, observations and analyses of small scale infiltration processes as well as continuous measurements of river discharges in West Africa are essential to ensure the reliability of future calibration for the infiltration parameterizations.

The AMMA (African Monsoon Multidisciplinary Analysis) program is dedicated to providing a better understanding of the West African monsoon and its influence on the physical, chemical and biological environment regionally and globally, as well as relating variability of this monsoon system to issues of health, water resources, food security and demography for West African nations. Within this framework, an intensive field campaign took place during the summer of 2006 to better document specific processes and weather systems at various key stages of this monsoon season. This campaign was embedded within a longer observation period that documented the annual cycle of surface and atmospheric conditions between 2005 and 2007. The present paper provides a large and regional scale overview of the 2006 summer monsoon season, that includes consideration of of the convective activity, mean atmospheric circulation and synoptic/intraseasonal weather systems, oceanic and land surface conditions, continental hydrology, dust concentration and ozone distribution. The 2006 African summer monsoon was a near-normal rainy season except for a large-scale rainfall excess north of 15° N. This monsoon season was also characterized by a 10-day delayed onset compared to climatology, with convection becoming developed only after 10 July. This onset delay impacted the continental hydrology, soil moisture and vegetation dynamics as well as dust emission. More details of some less-well-known atmospheric features in the African monsoon at intraseasonal and synoptic scales are provided in order to promote future research in these areas.

The aim of this paper is to improve the representation of root water uptake in the land surface scheme SECHIBA coupled to the LMD General Circulation Model (GCM). Root water uptake mainly results from the interaction between soil moisture and root profiles. Firstly, one aspect of the soil hydrology in SECHIBA is changed: it is shown that increasing the soil water storage capacity leads to a reduction in the frequency of soil water drought, but enhances the mean evapotranspiration. Secondly, the representation of the soil-vegetation interaction is improved by allowing a different root profile for each type of vegetation. The interaction between sub-grid scale variabilities in soil moisture and vegetation is also studied. The approach consists of allocating a separate soil water column to each vegetation type, thereby 'tiling' the grid square. However, the possibility of choosing the degree of soil moisture spatial heterogeneity is retained. These enhancements of the land surface system are compared within a number of GCM experiments.

Due to the lack of tools that can be easily used by practitioners, there is a need to develop acceptable embedded ways to assess children's fine motor skill development within early childhood education and care settings. This study examined the validity and reliability of a brief and ecologically valid fine motor assessment tool for preschool-aged children; the fine motor growth assessment (i.e., FINGA). Children's fine motor performance on FINGA was compared with widely-used and validated performance-based (Peabody Developmental Motor Scales 2nd edition) and informant-based (Ages and Stages Questionnaire 3rd edition) fine motor assessments. Ninety-one children [mean age (y) ± SD = 4.50y ± 0.68] were assessed within seven early childhood education and care services in New South Wales, Australia. Exploratory factor analyses (EFA) and linear regression analyses showed that FINGA had good internal consistency (EFA of 73%) and age sensitivity (B[subscript std] = 0.69, p < 0.001), and bivariate correlation analyses demonstrated good concurrent validity (rs from 0.69 to 0.84, ps < 0.001) against the two comparison assessments. Because of its brevity and ecological validity, the FINGA tool should be further examined as a routine fine motor skills assessment administered within the context of universal early childhood education and care service provision.

Abstract. Sub-seasonal to seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging but 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 Sub-seasonal to Seasonal Prediction (LS4P), as the first international grass-root 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, hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than forty groups worldwide have participated in this effort, including 21 Earth System Models, 9 regional climate models, and 7 data groups. This paper overviews 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 in the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation and its S2S prediction. 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; and ii) reanalysis data, that 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.

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.

There is need and opportunity for assessments that support quick and playful--yet also accurate, rigorous and developmentally sensitive--appraisals of early numeracy. Ideally, these should be accessible to those who have opportunity to support children's learning development and ultimately shift children's trajectories. The iPad-based "Early Years Toolbox (EYT) Early Numeracy" assessment was developed and evaluated to determine its validity and reliability, and appropriateness of use by researchers and preschool educators. Results from two studies with 246 children aged 3-5 years indicated the following: construct validity and internal consistency, concurrent validity with established measures, developmental sensitivity, test--retest reliability and highly comparable results whether used by a researcher or an educator. This yields a brief and playful assessment of early numeracy and a potential approach to develop broad-use early years assessments.

Background: Over the past few decades early self-regulation has been identified as foundational to positive learning and wellbeing trajectories. As a consequence, a wide range of approaches have been developed to capture children's developmental progress in self-regulation. Little is known, however, about whether and which of these are reliable indicators of future ability and risk for young children. Objective: This study examined measures from prominent approaches to self-regulation assessment (i.e., task-based, observation, adult-report) to determine: their structure; how these predict future academic school readiness in 3-5-year-old children, individually and if combined; and whether thresholds could be ascertained to reliably discriminate those children at risk of poor academic outcomes. Methods: Longitudinal analyses were conducted on start-of-year self-regulation data from 217 children in the final year of pre-school, using three prominent approaches to self-regulation assessment, and their end-of-year school readiness data. Data were subjected to path analysis, structural equation modelling and receiver operating characteristic curve analyses. Results: Start-of-year cognitive self-regulation indices--but not behavioral or emotional self-regulation indices--from each approach reliably predicted school readiness 7 months later, just prior to commencing school. Only when combined into a composite score was a threshold with sufficient sensitivity and specificity for predicting school readiness risk established; yet this provided better prediction of true-negative than true-positive cases. Conclusions: Taken together, these results suggest the importance of cognitive self-regulation in particular for school readiness, as measured here, although self-regulation is just one of the contributing factors to school readiness risk.

The question of when moral identity first develops in childhood deserves more considered investigation. In this article, we examine the claim that moral identity first emerges in middle-childhood (8-12 years). An approach is taken here whereby a tendency to attribute moral shame under conditions entailing moral identity failure is considered as indicating the presence of an emerging moral identity. Differences in the emotion attributions (i.e., ashamed, guilty, scared) of 7- and 9-year-olds under moral and non-moral identity failure conditions were examined. Results showing that older children tended to attribute shame under all identity failure conditions while younger children did not, and furthermore that both age groups tended to attribute predominantly guilt under accidental harm conditions, lend weight to the suggestion that moral identity may first emerge in middle-childhood. Implications of this study and its novel approach are discussed.

Many children with hearing loss have atypical social communication skills despite having age-appropriate speech and language. Graduate assessments in an early intervention program for children with hearing loss indicated that despite achieving language skills within typical limits for over a decade, social skills development was frequently delayed. Data gathered in 2007 and 2012 indicated the majority of children with hearing loss demonstrated poor acquisition of concepts linked to theory of mind (ToM), achieving either delayed or alternative acquisition patterns. A small-group 8-week social skills intervention program was subsequently implemented for graduating cohorts with the aim of developing and improving social interactions. In 2017, measures of ToM were collected for 15 children with hearing loss aged 4-6 years and compared to ToM 2007 and 2012 cohort data. An additional measure of social understanding and flexibility, a persuasion task, was also implemented. Although ToM skills for the majority of the 2017 cohort were found to be on par with hearing peers, and were better than skills demonstrated by the 2007 and 2012 graduates, ability to successfully participate in a socially significant persuasion task with a peer was delayed. Challenges and solutions to the development of age-appropriate social skills are proposed.

Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change.

The acquisition of theory of mind (ToM) -- the ability to attribute mental states to explain others' behaviors -- is a critical milestone in children's cognitive development. Previous research has established that deaf children experience significant delays in ToM compared to hearing children within the same culture. However, prior studies were restricted due to the reliance on work in deaf samples from Western cultures. Thus, to examine the pattern and rate of acquisition of ToM in a non-Western context, the present study compares the performance of deaf and hearing groups from the Philippines. This research reports on two studies (n = 209 and n = 42) that explore ToM performance among Filipino deaf and hearing individuals using a ToM scale. In study 1, deaf children aged 8 to 14 years demonstrated significant deficits in ToM relative to younger 3 to 7-year-olds and age-matched hearing controls. In study 2, a significantly greater proportion of deaf adolescents and young adults aged 15 to 22 years showed better ToM than the younger deaf group in study 1. Improved performance notwithstanding, the scores of the older deaf participants were still lower than those of the 8 to 14-year-old hearing children in study 1. Despite these delays, the pattern of ToM acquisition of both deaf and hearing Filipino groups mirrored that of Western cultures. Results are discussed in terms of the complex nature of communication within Filipino families and the limited access to conversations in families with deaf children.

Research increasingly acknowledges the importance of high quality interactions that support and extend children's thinking. Few measurement tools currently exist, however, to capture this specific aspect of process quality. The Sustained Shared Thinking and Emotional Wellbeing (SSTEW) scale was developed to assess interactional quality in early childhood education and care, and it includes dimensions of process quality based on developmental theories and practice in effective settings. This study compared ratings on the SSTEW and Early Childhood Environment Rating Scale -- Extension (ECERS-E) to consider the impact of varying levels of curricular and interactional quality on child development in 45 Australian pre-school centres; namely the language, numeracy and socio-behavioural development of 669 children at the end of their pre-school year. Results indicated a level of predictive validity for interactional quality ratings as measured by SSTEW which, while related to curricular quality ratings on ECERS-E, differed in associations across domains of child development.

This study examines associations between Australia's regulatory ratings of quality in early childhood education and care (ECEC)--the National Quality Standard (NQS)--and two research-based quality rating scales. The analytic sample consisted of 257 ECEC services across three Australian states. Results indicated (1) modest positive associations between NQS ratings and scale scores; (2) some specificity between NQS quality areas (educational programs and practice; relationships with children) and one research scale--the Sustained Shared Thinking and Emotional Wellbeing (SSTEW) scale; (3) variability in quality scales scores within each NQS designation; and (4) mitigation of these associations when the time-gap between ratings exceeded 24 months. Findings suggest NQS and research scales tap some common core of quality, yet capture different aspects of quality, suggesting both could be used to raise standards of quality in Australian preschools, where the research scales potentiate raising quality to even higher levels than NQS.

The Project for Intercomparison of Land-Surface Parameterization Schemes phase 2(d) experiment at Valdai, Russia, offers a unique opportunity to evaluate land surface schemes, especially snow and frozen soil parameterizations. Here, the ability of the 21 schemes that participated in the experiment to correctly simulate the thermal and hydrological properties of the soil on several different timescales was examined. Using observed vertical profiles of soil temperature and soil moisture, the impact of frozen soil schemes in the land surface models on the soil temperature and soil moisture simulations was evaluated. It was found that when soil-water freezing is explicitly included in a model, it improves the simulation of soil temperature and its variability at seasonal and interannual scales. Although change of thermal conductivity of the soil also affects soil temperature simulation, this effect is rather weak. The impact of frozen soil on soil moisture is inconclusive in this experiment due to the particular climate at Valdai, where the top 1 m of soil is very close to saturation during winter and the range for soil moisture changes at the time of snowmelt is very limited. The results also imply that inclusion of explicit snow processes in the models would contribute to substantially improved simulations. More sophisticated snow models based on snow physics tend to produce better snow simulations, especially of snow ablation. Hysteresis of snow-cover fraction as a function of snow depth is observed at the catchment but not in any of the models.

The Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) aims to improve understanding and modeling of land surface processes. PILPS phase 2(d) uses a set of meteorological and hydrological data spanning 18 yr (1966-83) from a grassland catchment at the Valdai water-balance research site in Russia. A suite of stand-alone simulations is performed by 21 land surface schemes (LSSs) to explore the LSSs' sensitivity to downward longwave radiative forcing, timescales of simulated hydrologic variability, and biases resulting from single-year simulations that use recursive spinup. These simulations are the first in PILPS to investigate the performance of LSSs at a site with a well-defined seasonal snow cover and frozen soil. Considerable model scatter for the control simulations exists. However, nearly all the LSS scatter in simulated root-zone soil moisture is contained within the spatial variability observed inside the catchment. In addition, all models show a considerable sensitivity to longwave forcing for the simulation of the snowpack, which during the spring melt affects runoff, meltwater infiltration, and subsequent evapotranspiration. A greater sensitivity of the ablation, compared to the accumulation, of the winter snowpaek to the choice of snow parameterization is found. Sensitivity simulations starting at prescribed conditions with no spinup demonstrate that the treatment of frozen soil (moisture) processes can affect the long-term variability of the models. The single-year recursive runs show large biases, compared to the corresponding year of the control run, that can persist through the entire year and underscore the importance of performing multiyear simulations.

Anticipating future interactions is characteristic of our everyday social experiences, yet has received limited empirical attention. Little is known about how children with autism spectrum disorder, known for their limitations in social interactive skills, engage in "social anticipation." We asked children with autism spectrum disorder and their typically developing counterparts to consider an interaction with another person in the near future. Our results suggest that children with autism spectrum disorder and typically developing children performed similarly when anticipating the age, gender, and possible questions of another person, but children with autism spectrum disorder struggled more to anticipate what they would say in response to an anticipated interaction. Furthermore, such responses were robustly associated with imaginative capacities in typically developing children but not children with autism spectrum disorder. Our findings suggest that the cognitive mechanisms of social anticipation may differ between these groups.

The empirical literature has presented inconsistent evidence for deficits in the recognition of basic emotion expressions in children with autism spectrum disorders (ASD), which may be due to the focus on research with relatively small sample sizes. Additionally, it is proposed that although children with ASD may correctly identify emotion expression they rely on more deliberate, more time-consuming strategies in order to accurately recognize emotion expressions when compared to typically developing children. In the current study, we examine both emotion recognition accuracy and response time in a large sample of children, and explore the moderating influence of verbal ability on these findings. The sample consisted of 86 children with ASD (M [subscript age] = 10.65) and 114 typically developing children (M[subscript age] = 10.32) between 7 and 13 years of age. All children completed a pre-test (emotion word-word matching), and test phase consisting of basic emotion recognition, whereby they were required to match a target emotion expression to the correct emotion word; accuracy and response time were recorded. Verbal IQ was controlled for in the analyses. We found no evidence of a systematic deficit in emotion recognition accuracy or response time for children with ASD, controlling for verbal ability. However, when controlling for children's accuracy in word-word matching, children with ASD had significantly lower emotion recognition accuracy when compared to typically developing children. The findings suggest that the social impairments observed in children with ASD are not the result of marked deficits in basic emotion recognition accuracy or longer response times. However, children with ASD may be relying on other perceptual skills (such as advanced word-word matching) to complete emotion recognition tasks at a similar level as typically developing children.

Links between young children's everyday use of mindful conversational skills and their success on laboratory tests of theory of mind understanding (ToM) were evaluated. Using published scales, teachers rated the conversational behavior and shyness of 129 children aged 60 to 101 months (M = 78·8 months) who were in their first years of primary school. The children also took batteries of first- and second-order false-belief tests along with tests of emotion understanding and general language ability. Correlational and regression analyses showed that performance on false-belief tests of ToM significantly predicted children's competence at reading others' minds in their everyday conversational interactions. Furthermore, these links transcended individual differences in language ability, shy personality, emotion understanding, and age. These findings augment and extend a growing body of evidence linking performance on laboratory ToM tests to socially competent real-world behavior.

The understanding of emotions based on counterfactual reasoning was studied in children with high-functioning autism spectrum disorders ("n" = 71) and in typically developing children ("n" = 71), aged 6-12 years. Children were presented with eight stories about two protagonists who experienced the same positive or negative outcome, either due to their own action or by default. Relative to the comparison group, children with high-functioning autism spectrum disorder were poor at explaining emotions based on "downward" counterfactual reasoning (i.e. contentment and relief). There were no group differences in "upward" counterfactual reasoning (i.e. disappointment and regret). In the comparison group, second-order false-belief reasoning was related to children's understanding of second-order counterfactual emotions (i.e. regret and relief), while children in the high-functioning autism spectrum disorder group relied more on their general intellectual skills. Results are discussed in terms of the different functions of counterfactual reasoning about emotion and the cognitive style of children with high-functioning autism spectrum disorder.

Background: The sociocommunicative problems in autism spectrum disorder (ASD) are traditionally linked to impairments in Theory of Mind (ToM), the ability to ascribe mental states to others. Although ToM impairments are consistently reported in young children with ASD, findings on more advanced ToM understanding in older individuals with high-functioning ASD (HFASD) are less straightforward. Therefore, we assessed the advanced ToM abilities of a large sample of school-aged children and adolescents with HFASD ("n" = 194; 6-20 years) and compared them to a typically developing (TD) comparison group ("n" = 60). Methods: Participants' advanced ToM was assessed with five social stories containing second-order false beliefs, display rules, double bluff, faux pas, and sarcasm. Results: Participants with HFASD performed equally well on each of the ToM stories as their TD peers. Consistent age effects were noticed with adolescents outperforming the children. Furthermore, advanced ToM was positively associated with participants' age, verbal abilities, and general reasoning abilities. Conclusions: Counter to what the ToM theory of ASD would predict, school-aged children and adolescents with HFASD seem to be able to master the theoretical principles of advanced mental state reasoning. However, they may still fail to apply these theoretical principles during everyday social interactions. (Contains 3 tables.)

We evaluated the utility of a brief, seven-item, teacher-rated Peer Social Maturity Scale (PSMAT). In Study 1, teachers of 138 Australian children (ranging from 5 to 8?years and 5?months old) in kindergarten and Grades 1 and 2 rated their pupils' social maturity using the PSMAT and their classroom social skills via the Social Skills Rating System (SSRS). The PSMAT showed excellent internal consistency and a significant overall correlation with the SSRS social skills scale. Study 2 involved a new longitudinal sample of children who were rated by different classroom teachers in kindergarten and Grades 1 and 2 of full-time primary school. Consistent with Study 1, at all three time points, PSMAT scores were highly correlated with SSRS social skills scores. PSMAT scores also correlated significantly with peer-rated social preference. These studies confirm that the PSMAT is a reliable and valid assessment of children's social maturity within their classroom peer groups.

In a socially diverse sample of 206 infant-mother pairs, we investigated predictors of infants' attachment security at 15 months, with a particular emphasis on mothers' tendency to comment appropriately or in a non-attuned manner on their 8-month-olds' internal states (so-called mind-mindedness). Multinomial logistic regression analyses showed that higher scores for appropriate mind-related comments and lower scores for non-attuned mind-related comments distinguished secure-group mothers from their counterparts in the insecure-avoidant, insecure-resistant, and insecure-disorganized groups. Higher scores for appropriate mind-related comments and lower scores for non-attuned mind-related comments also independently predicted dichotomous organized/disorganized attachment. General maternal sensitivity predicted neither attachment security nor organization, although sensitivity was found to relate to dichotomous secure/insecure attachment specifically in the context of low socioeconomic status. The findings highlight how appropriate and non-attuned mind-related comments make independent contributions to attachment and suggest that mind-mindedness is best characterized as a multidimensional construct.

Responses to an unfamiliar adult were examined in infants of mothers with social phobia (N = 79) and infants of nonanxious comparison mothers (N = 77) at 10 and 14 months in a social referencing paradigm. On each occasion, a female stranger first interacted with the mother and then approached and interacted with the infant. Over time, infants of mothers with social phobia showed increasing avoidance of the stranger, particularly when they were behaviorally inhibited. In boys, maternal social phobia was associated with increasing fearful responses. Infant avoidance was predicted by expressed maternal anxiety and low levels of encouragement to interact with the stranger. The findings are discussed in relation to theories concerning the intergenerational transmission of social anxiety.

This paper presents a selective review of the literature addressing the influence of young children's conversational environments and interactions on their psychological understanding of persons. Our dual purposes are to reveal some consensus on the current state of knowledge and to foster a programmatic approach to future research. The initial sections clarify what is meant by "conversation" and describe the nature of theory-of-mind development. We adopt the term "socio-cognitive development" to convey the fact that children's theory-of-mind understanding becomes more elaborate and flexible throughout childhood, and we discuss issues of measurement. The following sections deal directly with the influence of conversational environments and interactions on children's socio-cognitive understanding. We present findings that suggest that conversational interactions are of fundamental importance for the development of children's socio-cognitive understanding, and we examine the particular aspects of the former that have been shown to be the most effective in promoting the latter. We discuss the relationship between children's conversational references to thoughts and feelings and experimental assessments of their socio-cognitive understanding, and we offer a detailed list of considerations for future research. In the remaining sections, we highlight the important roles of (i) young children's expanding linguistic competence and (ii) their relationships, and we discuss implications for individual differences in children's developing social competence.

This study examines the contribution of children's linguistic ability and mothers' use of mental-state language to young children's understanding of false belief and their subsequent ability to make belief-based emotion attributions. In Experiment 1, children (N = 51) were given three belief-based emotion-attribution tasks. A standard task in which the protagonist was a story character and the emotional outcomes were imagined, and two videos in which the story protagonist was a real infant and the emotional outcomes were observable (high and low expressed emotion conditions). Children's verbal ability (semantic competence) was also measured. In Experiment 2, children (N = 75) were given two belief-based emotion tasks: the standard story task and the high expressed emotion video. In addition, children's verbal ability (syntactic competence) and mothers' use of mental-state attributes when describing their children were also measured. The results showed that: (1) the lag between understanding false belief and emotion attribution was a stable feature of children's reasoning across the three tests; and (2) children who were more linguistically advanced and whose mothers' described them in more mentalistic terms were more likely to understand the association between false belief and emotion. The findings underline the continuing importance of verbal ability and linguistic input for children's developing theory-of-mind understanding, even after they display an understanding of false belief.