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Given the ever increasing spatial resolution of climate models and the significant role of lakes on the regional climate, it becomes important to represent water bodies in climate models. Such developments have started in the IPSL (Institut Pierre Simon Laplace) climate model and its land surface component, ORganizing Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE), with the Freshwater Lake model, FLake. To answer the questions raised by these new developments, such as the lake differentiation and related model parameters, we analyze spatial distributions of lake characteristics in the whole world to perform a global sensitivity analysis of the FLake parameters. As a result, three different climates and four lake depth configurations were selected as test cases. The Sobol method as sensitivity analysis based on variance decomposition was chosen to rank parameters impact on the model output, that is, lake surface water temperature, latent and sensible heat fluxes. We focus on the 11 parameters of the FLake model, which are the lake depth, the albedo and light extinction coefficient of water, snow, and ice respectively, the fetch, and the relaxation coefficient of the thermocline shape factor. The results show different sensitivity features according to the lake type and climate. The dominant role and time varying contribution of the lake depth, radiative parameters (albedo, light extinction coefficient) and thermocline relaxation coefficient linked to the atmospheric conditions, were clearly highlighted. These findings will lead us to distinguish between different lake categories in each grid cell of ORCHIDEE in the future implementation. Plain Language Summary: Lakes are often neglected in climate modeling because their spatial extent does not exceed 4% of the Earth land area. But given the ever increasing spatial resolution of climate models and the significant role of lakes on the regional climate, it becomes important to represent water bodies in climate models. Such developments have started in the French IPSL climate model with the implementation of a one‐dimensional freshwater lake model (FLake). To answer the questions raised by these new developments, such as the lake differentiation and related model parameters, we analyze spatial distributions of lake characteristics in the whole world to perform a global sensitivity analysis of the model parameters. Our results show the respective roles of the depth, fetch, thermocline, sediment, and radiative (albedo and light extinction coefficient) parameters in different climate conditions and at different time scales. Some avenues for further implementation and model parameters calibration are finally given. Key Points: FLake model parameters sensitivities on lake surface temperature and sensible and latent heat fluxes are analyzedParameter sensitivities to surface variables vary with depth, climate, and seasonRespective roles of depth, fetch, surface albedo, light extinction, and thermocline shape coefficients are quantified [ABSTRACT FROM AUTHOR]

In China, irrigation is widespread in 40.7% cropland to sustain crop yields. By its action on water cycle, irrigation affects water resources and local climate. In this study, a new irrigation module, including flood and paddy irrigation technologies, was developed in the ORCHIDEE‐CROP land surface model which describes crop phenology and growth in order to estimate irrigation demands over China from 1982 to 2014. Three simulations were performed including NI (no irrigation), IR (with irrigation limited by local water resources), and FI (with irrigation demand fulfilled). Observations and census data were used to validate the simulations. Results showed that the estimated irrigation water withdrawal (W) based on IR and FI scenarios bracket statistical W with fair spatial agreements (r=0.68±0.07; p<0.01). Improving irrigation efficiency was found to be the dominant factor leading to the observed W decrease. By comparing simulated total water storage (TWS) with GRACE observations, we found that simulated TWS with irrigation well explained the TWS variation over China. However, our simulation overestimated the seasonality of TWS in the Yangtze River Basin due to ignoring regulation of artificial reservoirs. The observed TWS decrease in the Yellow River Basin caused by groundwater depletion was not totally captured in our simulation, but it can be inferred by combining simulated TWS with census data. Moreover, we demonstrated that land use change tended to drive W locally but had little effect on total W over China due to water resources limitation. Key Points: A new irrigation module is developed in ORCHIDEE including drip, flood, and paddy irrigationSimulations can explain broad scale patterns and the trends of irrigation amount from census dataIrrigation efficiency and land use change are key factors affecting the trend of irrigation [ABSTRACT FROM AUTHOR]

The ORCHIDEE land surface model has recently been updated to improve the representation of high-latitude environments. The model now includes improved soil thermodynamics and the representation of permafrost physical processes (soil thawing and freezing), as well as a new snow model to improve the representation of the seasonal evolution of the snow pack and the resulting insulation effects. The model was evaluated against data from the experimental sites of the WSibIso-Megagrant project (www.wsibiso.ru). ORCHIDEE was applied in stand-alone mode, on two experimental sites located in the Yamal Peninsula in the northwestern part of Siberia. These sites are representative of circumpolar-Arctic tundra environments and differ by their respective fractions of shrub/tree cover and soil type. After performing a global sensitivity analysis to identify those parameters that have most influence on the simulation of energy and water transfers, the model was calibrated at local scale and evaluated against in situ measurements (vertical profiles of soil temperature and moisture, as well as active layer thickness) acquired during summer 2012. The results show how sensitivity analysis can identify the dominant processes and thereby reduce the parameter space for the calibration process. We also discuss the model performance at simulating the soil temperature and water content (i.e., energy and water transfers in the soil-vegetationatmosphere continuum) and the contribution of the vertical discretization of the hydrothermal properties. This work clearly shows, at least at the two sites used for validation, that the new ORCHIDEE vertical discretization can represent the water and heat transfers through complex cryogenic Arctic soils--soils which present multiple horizons sometimes with peat inclusions. The improved model allows us to prescribe the vertical heterogeneity of the soil hydrothermal properties. [ABSTRACT FROM AUTHOR]

In Earth system modelling, a description of the energy budget of the vegetated surface layer is fundamental as it determines the meteorological conditions in the planetary boundary layer and as such contributes to the atmospheric conditions and its circulation. The energy budget in most Earth system models has been based on a big-leaf approach, with averaging schemes that represent in-canopy processes. Furthermore, to be stable, that is to say, over large time steps and without large iterations, a surface layer model should be capable of implicit coupling to the atmospheric model. Surface models with large time steps, however, have difficulties in reproducing consistently the energy balance in field observations. Here we outline a newly developed numerical model for energy budget simulation, as a component of the land surface model ORCHIDEE-CAN (Organising Carbon and Hydrology In Dynamic Ecosystems - CANopy). This new model implements techniques from single-site canopy models in a practical way. It includes representation of incanopy transport, a multi-layer long-wave radiation budget, height-specific calculation of aerodynamic and stomatal conductance, and interaction with the bare-soil flux within the canopy space. Significantly, it avoids iterations over the height of the canopy and so maintains implicit coupling to the atmospheric model LMDz (Laboratoire de Météorologie Dynamique Zoomed model). As a first test, the model is evaluated against data from both an intensive measurement campaign and longer-term eddy-covariance measurements for the intensively studied Eucalyptus stand at Tumbarumba, Australia. The model performs well in replicating both diurnal and annual cycles of energy and water fluxes, as well as the vertical gradients of temperature and of sensible heat fluxes. [ABSTRACT FROM AUTHOR]

Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as 1 of 13 essential climate variables targeted for research development. The LC_CCI was implemented in three phases: first responding to a survey of user needs; developing a global, moderate-resolution land cover data set for three time periods, or epochs (2000, 2005, and 2010); and the last phase resulting in a user tool for converting land cover to plant functional type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to plant functional types (PFTs). The translation was performed as part of consultative process among map producers and users, and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three earth system modeling teams shows significant differences between the LC_CCI PFT data set and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land-atmosphere interactions. The main difference between the new LC_CCI product and PFT data sets used currently by three different dynamic global vegetation modeling teams is a reduction in high-latitude grassland cover, a reduction in tropical tree cover and an expansion in temperate forest cover in Europe. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as phase 2 of the European Space Agency CCI program continues. [ABSTRACT FROM AUTHOR]

Processes that describe the distribution of vegetation and ecosystem succession after disturbance are an important component of dynamic global vegetation models (DGVMs). The vegetation dynamics module (ORC-VD) within the process-based ecosystem model ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) has not been updated and evaluated since many years and is known to produce unrealistic results. This study presents a new parameterization of ORC-VD for mid- to high-latitude regions in the Northern Hemisphere, including processes that influence the existence, mortality and competition between tree functional types. A new set of metrics is also proposed to quantify the performance of ORC-VD, using up to five different data sets of satellite land cover, forest biomass from remote sensing and inventories, a data-driven estimate of gross primary productivity (GPP) and two gridded data sets of soil organic carbon content. The scoring of ORC-VD derived from these metrics integrates uncertainties in the observational data sets. This multi-data set evaluation framework is a generic method that could be applied to the evaluation of other DGVM models. The results of the original ORC-VD published in 2005 for mid- to high-latitudes and of the new parameterization are evaluated against the above-described data sets. Significant improvements were found in the modeling of the distribution of tree functional types north of 40° N. Three additional sensitivity runs were carried out to separate the impact of different processes or drivers on simulated vegetation vegetation distribution, including soil freezing which limits net primary production through soil moisture availability in the root zone, elevated CO2 concentration since 1850, and the effects of frequency and severity of extreme cold events during the spin-up phase of the model. [ABSTRACT FROM AUTHOR]

Improvements in the evaluation of land surface models would translate into more reliable predictions of future climate changes, as significant uncertainties persist in the quantification and representation of the relative contributions of soil and vegetation to the water and energy cycles. In this paper, we investigate the usefulness of water stable isotopes in land surface models studying land surface processes. To achieve this, we implemented 18O and ²H and the computation of the oxygen (δ18O) and deuterium (δD) stable isotope composition of soil and leaf water pools in a recent version of the land surface model ORCHIDEE. We performed point-wise simulations with this new model and evaluated its performance on vertical profiles of soil water isotope ratios measured in summer 2012 at four experimental sites located in a boreal region of the Artic zone of western Siberia. The model performed relatively well in simulating some features of the δ18O soil profiles, but poorly reproduced the d-excess profiles, at all four stations. The response of the simulated δ18O profiles to variations in key hydrological parameters revealed the importance of the choice of a correct infiltration pathway in ORCHIDEE. Our results show also that the strength of the evaporative enrichment signal plays a role in shaping the profiles, too and, therefore, the relevance of the vegetation and bare soil characterization. We investigated furthermore to which extent we are able to determine the relative contribution of the evaporation to the evapotranspiration. This study's results confirm that the use of water stable isotopes measurements helps constrain the representation of key land surface processes in land surface models. [ABSTRACT FROM AUTHOR]

Processes that describe the distribution of vegetation and ecosystem succession after disturbance are an important component of dynamic global vegetation models (DGVMs). The vegetation dynamics module (ORC-VD) within the process-based ecosystem model ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) has not been updated and evaluated since many years and does not match the progress in modeling the rest of the physical and biogeochemical processes. Therefore, ORC-VD is known to produce unrealistic results. This study presents a new parameterization of ORC-VD for mid-to-high latitude regions in the Northern Hemisphere, including processes that influence the existence, mortality and competition between tree functional types. A new set of metrics is also proposed to quantify the performance of ORC-VD, using up to five different datasets of satellite land cover, forest biomass from remote sensing and inventories, a data-driven estimate of gross primary productivity (GPP) and two gridded datasets of soil organic carbon content. The scoring of ORC-VD derived from these metrics integrates uncertainties in the observational datasets. This multi-dataset evaluation framework is a generic method that could be applied to the evaluation of other DGVM models. The results of the original ORC-VD published in 2005 for mid-to-high latitudes and of the new parameterization are evaluated against the above-described datasets. Significant improvements were found in the modeling of the distribution of tree functional types north of 40° N. Three additional sensitivity runs were carried out to separate the impact of different processes or drivers on simulated vegetation distribution, including soil freezing which limits net primary production through soil moisture availability in the root zone, elevated CO2 concentration since 1850, and the return frequency of cold climate extremes causing tree mortality during the spin-up phase of the model. [ABSTRACT FROM AUTHOR]

High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover data sets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing Earth system models. Earth system models also require specific land cover classification systems based on plant functional types (PFTs), rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover data sets against one another and with auxiliary data to identify key uncertainties that contribute to variability in PFT classifications that would introduce errors in Earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to represent the water and carbon cycles in northern latitudes better. Updated land cover data sets are critical for improving and maintaining the relevance of Earth system models for assessing climate and human impacts onbiogeochemistry and biophysics. [ABSTRACT FROM AUTHOR]

Winter CO2 fluxes represent an important component of the annual carbon budget in northern ecosystems. Understanding winter respiration processes and their responses to climate change is also central to our ability to assess terrestrial carbon cycle and climate feedbacks in the future. However, the factors influencing the spatial and temporal patterns of winter ecosystem respiration (Reco) of northern ecosystems are poorly understood. For this reason, we analyzed eddy covariance flux data from 57 ecosystem sites ranging from ~35° N to ~70° N. Deciduous forests were characterized by the highest winter Reco rates (0.90±0.39 gCm-2 d-1 ), when winter is defined as the period during which daily air temperature remains below 0° C. By contrast, arctic wetlands had the lowest winter Reco rates (0.02±0.02 gCm-2 d-1 ). Mixed forests, evergreen needle-leaved forests, grasslands, croplands and boreal wetlands were characterized by intermediate winter Reco rates (g Cm -2d-1 ) of 0.70(±0.33), 0.60(±0.38), 0.62(±0.43), 0.49(±0.22) and 0.27(±0.08), respectively. Our cross site analysis showed that winter air (Tair) and soil (Tsoil) temperature played a dominating role in determining the spatial patterns of winter R eco in both forest and managed ecosystems (grasslands and croplands). Besides temperature, the seasonal amplitude of the leaf area index (LAI), inferred from satellite observation, or growing season gross primary productivity, which we use here as a proxy for the amount of recent carbon available for R eco in the subsequent winter, played a marginal role in winter CO2 emissions from forest ecosystems. We found that winter R eco sensitivity to temperature variation across space (Q S) was higher than the one over time (interannual, Q T). This can be expected because Q S not only accounts for climate gradients across sites but also for (positively correlated) the spatial variability of substrate quantity. Thus, if the models estimate future warming impacts on R eco based onQ S rather thanQ T, this could overestimate the impact of temperature changes. [ABSTRACT FROM AUTHOR]

Land water storage plays a fundamental role in the West African water cycle and has an important impact on climate and on the natural resources of this region. However, measurements of land water storage are scarce at regional and global scales and especially in poorly instrumented endorheic regions, such as most of the Sahel, where little useful information can be derived from river flow measurements and basin water budgets. The Gravity Recovery and Climate Experiment (GRACE) satellite mission provides an accurate measurement of the terrestrial gravity field variations from which land water storage variations can be derived. However, their retrieval is not straightforward, and different methods are employed, which results in different water storage GRACE products. On the other hand, water storage can be estimated by land surface modeling forced with observed or satellite-based boundary conditions, but such estimates can be highly model dependent. In this study, land water storage by six GRACE products and soil moisture estimations by nine land surface models (run within the framework of the African Monsoon Multidisciplinary Analysis Land Surface Intercomparison Project (ALMIP)) are evaluated over West Africa, with a particular focus on the Sahelian area. The water storage spatial distribution, including zonal transects, its seasonal cycle, and its and interannual variability, are analyzed for the years 2003-2007. Despite the nonnegligible differences among the various GRACE products and among the different models, a generally good agreement between satellite and model estimates is found over the West Africa study region. In particular, GRACE data are shown to reproduce well the water storage interannual variability over the Sahel for the 5 year study period. The comparison between satellite estimates and ALMIP results leads to the identification of processes needing improvement in the land surface models. In particular, our results point out the importance of correctly simulating slow water reservoirs as well as evapotranspiration during the dry season for accurate soil moisture modeling over West Africa. [ABSTRACT FROM AUTHOR]

The Somme River Basin is located above a chalk aquifer and the discharge of the somme River is highly influenced by groundwater inflow (90% of river discharge is baseflow). In 2001, the Somme River Basin suffered from a major flood causing damages estimated to 100 million euro (Deneux and Martin, 2001). The purpose of the present research is to evaluate the ability of four hydrologic models to reproduce flood events in the Somme River Basin over an 18-year period, by comparison with observed river discharge and piezometric level as well as satellite-derived extents of flooded area. The models used differ in their computation of surface water budget and in their representation of saturated and unsaturated zones. One model needed structural modification to be able to accurately simulate the riverflows of the Somme river. The models obtained fair to good simulations of the observed piezometric levels, but they all overestimate the piezometric level after flooding, possibly because of a simplistic representation of deep unsaturated flow. Models differ in their annual partition of the infiltration of water within the root zone (mostly driven by simulated evapotranspiration), but these differences are attenuated by water transfers within the saturated and unsaturated zone. As a consequence, the inter-model dispersion of the computed annual baseflow is reduced. The aquifer overflow areas simulated during flooding compare well with local data and satellite images. The models showed that this overflow occurs almost every year in the same areas (in floodplain), and that the flooding of 2001 was characterized by an increase in the quantity of the overflow and not much by a spreading of the overflow areas. Inconsistencies between river discharge and piezometric levels suggest that further investigation are needed to estimate the relative influence of unsaturated and saturated zones on the hydrodynamics of the Somme River Basin. [ABSTRACT FROM AUTHOR]

The Somme River Basin is located above a chalk aquifer and the discharge of the somme River is highly influenced by groundwater inflow (90% of river discharge is baseflow). In 2001, the Somme River Basin suffered from a major flood causing damages estimated to 100 million Euro (Deneux and Martin, 2001). The purpose of the present research is to evaluate the ability of four hydrologic models to reproduce flood events in the Somme River Basin over an 18-year period, by comparison with observed river discharge and piezometric head as well as satellite-derived extents of the flooded area. The models used differ in their computation of the surface water budget and in their representation of saturated and unsaturated zones. One model needed structural modification to be able to accurately simulate the riverflows of the Somme river. The models obtained fair to good simulations of the observed piezometric heads, but they all overestimate the piezometric level after flooding, possibly because of a simplistic representation of the deep unsaturated flow. Models differ in their annual partition of the infiltration of water within the root zone (mostly driven by simulated evapotranspiration), but these differences are attenuated by water transfers within the saturated and unsaturated zone. As a consequence, the inter-model dispersion of the computed annual baseflow is reduced. The aquifer overflow areas simulated during flooding compare well with local data and satellite images. The models showed that this overflow occurs almost every year in the same areas (in the floodplain), and that the flooding of 2001 was characterized by an increase in the quantity of the overflow and not much by a spreading of the overflow areas. Inconsistencies between river discharge and piezometric levels suggest that further investigation are needed to estimate the relative influence of unsaturated and saturated zones on the hydrodynamics of the Somme River Basin. [ABSTRACT FROM AUTHOR]

Land-atmosphere feedbacks, which are particularly important over the Sahel during the West African Monsoon (WAM), partly depend on a large range of processes linked to the land surface hydrology and the vegetation heterogeneities. This study focuses on the evaluation of a new land surface hydrology within the Noah-WRF land-atmosphere-coupled mesoscale model over the Sahel. This new hydrology explicitly takes account for the Dunne runoff using topographic information, the Horton runoff using a Green-Ampt approximation, and land surface heterogeneities. The previous and new versions of Noah-WRF are compared against a unique observation dataset located over the Dantiandou Kori (Niger). This dataset includes dense rain gauge network, surfaces temperatures estimated from MSG/SEVIRI data, surface soil moisture mapping based on ASAR/ENVISAT C-band radar data and in situ observations of surface atmospheric and land surface energy budget variables. Generally, the WAM is reasonably reproduced by Noah-WRF even if some limitations appear throughout the comparison between simulations and observations. An appreciable improvement of the model results is also found when the new hydrology is used. This fact seems to emphasize the relative importance of the representation of the land surface hydrological processes on the WAM simulated by Noah-WRF over the Sahel. [ABSTRACT FROM AUTHOR]

The analysis of feedbacks between continental surfaces and the atmosphere is one of the key factors to understanding African Monsoon dynamics. For this reason, the monitoring of surface parameters, in particular soil moisture, is very important. Satellite remote sensing appears to be the most suitable means of obtaining data relevant to such parameters. The present paper presents a methodology applied to the mapping and monitoring of surface soil moisture over the Kori Dantiandou region in Niger, using data provided by the ASAR/ENVISAT radar instrument. The study is based on 15 sets of ASAR/ENVISAT C-band radar data, acquired during the 2004 and 2005 rainy seasons. Simultaneously with radar acquisitions, ground soil moisture measurements were carried out in a large number of test fields. Soil moisture was estimated only for fields with bare soil or low-density vegetation, using low-incidence-angle radar data (IS1 configuration). A mask was developed, using SPOT/HRV data and DTM, for use over areas characterized by high-density vegetation cover, pools, and areas with high slopes. Soil moisture estimations are based on horizontal- and vertical-polarization radar data. In order to double the temporal frequency of soil moisture estimations, IS2 data were used with IS1 data, with all data normalized to a single incidence angle. A high correlation is observed between in situ measurements and processed radar data. An empirical inversion technique is proposed, to estimate surface soil moisture from dual-polarization data with a spatial resolution of approximately 1 km. Surface soil moisture maps are presented for all the studied sites, at various dates in 2004 and 2005. Of particular interest, these maps reveal convective precipitation scales associated with strong spatial variations in surface soil moisture. [ABSTRACT FROM AUTHOR]

In the remote sensing domain, the combination of multi-scale satellite data appears as a new challenge for the signal processing community. This approach will lead to strong advances in Earth monitoring and continental land cover classifications by use of the complementary of the data presenting either high spatial resolution or high time repetitiveness. For the modelling of the mixed feature of the low spatial resolution pixels, and those of the partial ignorance (class confusion) when time information is not sufficient, we propose an algorithm based on the Dempster-Shafer evidence theory, which allows to model both ignorance and imprecision, and to consider compound hypotheses such as unions of classes. It has been applied on simulated data and actual data (SPOT/HRV image and NOAA/AVHRR series), and in both cases, the results show unambiguously the major improvement brought by such a data fusion, and the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Seasonal snow cover in the Northern Hemisphere is the largest component of the terrestrial cryosphere and plays a major role in the climate system through strong positive feedbacks related to albedo. The snow-albedo feedback is invoked as an important cause for the polar amplification of ongoing and projected climate change, and its parameterization across models is an important source of uncertainty in climate simulations. Here, instead of developing a physical snow albedo scheme, we use a direct insertion approach to assimilate satellite-based surface albedo during the snow season (hereafter as snow albedo assimilation) into the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms) and assess the influences of such assimilation on offline and coupled simulations. Our results have shown that snow albedo assimilation in both ORCHIDEE and ORCHIDEE-LMDZ (a general circulation model of Laboratoire de Météorologie Dynamique) improve the simulation accuracy of mean seasonal (October throughout May) snow water equivalent over the region north of 40 degrees. The sensitivity of snow water equivalent to snow albedo assimilation is more pronounced in the coupled simulation than the offline simulation since the feedback of albedo on air temperature is allowed in ORCHIDEE-LMDZ. We have also shown that simulations of air temperature at 2 meters in ORCHIDEE-LMDZ due to snow albedo assimilation are significantly improved during the spring in particular over the eastern Siberia region. This is a result of the fact that high amounts of shortwave radiation during the spring can maximize its snow albedo feedback, which is also supported by the finding that the spatial sensitivity of temperature change to albedo change is much larger during the spring than during the autumn and winter. In addition, the radiative forcing at the top of the atmosphere induced by snow albedo assimilation during the spring is estimated to be -2.50 W m-2, the magnitude of which is almost comparable to that due to CO2 (2.83 W m-2) increases since 1750. Our results thus highlight the necessity of realistic representation of snow albedo in the model and demonstrate the use of satellite-based snow albedo to improve model behaviors, which opens new avenues for constraining snow albedo feedback in earth system models.

The impact of climate warming on the advancement of plant spring phenology has been heavily investigated over the last decade and there exists great variability among plants in their phenological sensitivity to temperature. However, few studies have explicitly linked phenological sensitivity to local climate variance. Here, we set out to test the hypothesis that the strength of phenological sensitivity declines with increased local spring temperature variance, by synthesizing results across ground observations. We assemble ground-based long-term (20-50 years) spring phenology database (PEP725 database) and the corresponding climate dataset. We find a prevalent decline in the strength of phenological sensitivity with increasing local spring temperature variance at the species level from ground observations. It suggests that plants might be less likely to track climatic warming at locations with larger local spring temperature variance. This might be related to the possibility that the frost risk could be higher in a larger local spring temperature variance and plants adapt to avoid this risk by relying more on other cues (e.g., high chill requirements, photoperiod) for spring phenology, thus suppressing phenological responses to spring warming. This study illuminates that local spring temperature variance is an understudied source in the study of phenological sensitivity and highlight the necessity of incorporating this factor to improve the predictability of plant responses to anthropogenic climate change in future studies., (© 2013 John Wiley & Sons Ltd.)

Measurement of radioactive dose rates in fine sediment that has recently deposited on channel bed-sand provides a solution to address the lack of continuous river monitoring in Fukushima Prefecture after Fukushima Dai-ichi nuclear power plant (FDNPP) accident. We show that coastal rivers of Eastern Fukushima Prefecture were rapidly supplied with sediment contaminated by radionuclides originating from inland mountain ranges, and that this contaminated material was partly exported by typhoons to the coastal plains as soon as by November 2011. This export was amplified during snowmelt and typhoons in 2012. In 2013, contamination levels measured in sediment found in the upper parts of the catchments were almost systematically lower than the ones measured in nearby soils, whereas their contamination was higher in the coastal plains. We thereby suggest that storage of contaminated sediment in reservoirs and in coastal sections of the river channels now represents the most crucial issue.