The Community Land Model (CLM) includes a large variety of parameterizations, also for flow in the unsaturated zone and soil properties. Soil properties introduce uncertainties into land surface model predictions. In this paper, soil moisture and soil properties are updated for the coupled CLM and Community Microwave Emission Model (CMEM) by the Local Ensemble Transform Kalman Filter (LETKF) and the state augmentation method. Soil properties are estimated through the update of soil textural properties and soil organic matter density. These variables are used in CLM for predicting the soil moisture retention characteristic and the unsaturated hydraulic conductivity, and the soil texture is used in CMEM to calculate the soil dielectric constant. The following scenarios were evaluated for the joint state and parameter estimation with help of synthetic L-band brightness temperature data assimilation: (i) the impact of joint state and parameter estimation; (ii) updating of soil properties in CLM alone, CMEM alone or both CLM and CMEM; (iii) updating of soil properties without soil moisture update; (iv) the observation localization of LETKF. The results show that the characterization of soil properties through the update of textural properties and soil organic matter density can strongly improve with assimilation of brightness temperature data. The optimized soil properties also improve the characterization of soil moisture, soil temperature, actual evapotranspiration, sensible heat flux, and soil heat flux. The best results are obtained if the soil properties are updated only. The coupled CLM and CMEM model is helpful for the parameter estimation. If soil properties are biased, assimilation of soil moisture data with only state updates increases the root mean square error for evapotranspiration, sensible heat flux, and soil heat flux.

More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a simulation model. Existing approaches can be used to simultaneously update several model states and model parameters if applicable. In other words, the basic principles for multivariate data assimilation are already available. We argue that a better understanding of the measurement errors for different observation types, improved estimates of observation bias and improved multiscale assimilation methods for data which scale nonlinearly is important to properly weight them in multiscale multivariate data assimilation. In this context, improved cross-validation of different data types, and increased ground truth verification of remote sensing products are required.

Northeastern China has the second largest expanse of permafrost in China, primarily known as Xing'an-Baikal permafrost. Located on the southeastern edges of the Eurasian cryolithozone, the permafrost is thermally unstable and ecologically sensitive to external changes. The combined impacts of climatic, environmental, and anthropogenic changes cause 3-dimensional degradation of the permafrost. To predict these changes on the southern limit and ground temperature of permafrost in Northeastern China, an equivalent latitude model (ELM) for the mean annual ground surface temperature (MAGSTs) was proposed, and further improved to take into account of the influences of vegetation and snow-cover based on observational data and using the SHAW model. Using the finite element method and assuming a climate warming rate of 0.048°C a 1 , the ELM was combined with the unsteady-state heat conduction model to simulate permafrost temperatures at present, and to predict those after 50 and 100 a. The results indicate that at present, sporadic permafrost occurs in the zones with MAGSTs of 1.5°C or colder, and there would still be a significant presence of permafrost in the zones with the present MAGSTs of 0.5°C or colder after 50 a, and in those of 0.5°C or colder after 100 a. Furthermore, the total areal extent of permafrost would decrease from 2.57×10 5 km 2 at present to 1.84×10 5 km 2 after 50 a and to 1.29×10 5 km 2 after 100 a, i.e., a reduction of 28.4% and 49.8% in the permafrost area, respectively. Also the permafrost would degrade more substantially in the east than in the west. Regional warming and thinning of permafrost would also occur. The area of stable permafrost (mean annual ground temperature, or MAGT≤1.0°C) would decrease from present 1.07×10 5 to 8.8×10 4 km 2 after 50 a, and further decrease to 5.6×10 4 km 2 after 100 a. As a result, the unstable permafrost and seasonally frozen ground would expand, and the southern limit of permafrost would shift significantly northwards. The changes in the permafrost environment may adversely affect on ecological environments and engineering infrastructures in cold regions. Avoidance of unnecessary anthropogenic changes in permafrost conditions is a practical approach to protect the permafrost environment. permafrost, Northeastern China, climate change, equivalent latitude model (ELM), predictionCitation:

Abstract. SMOS (Soil Moisture and Ocean Salinity mission) brightness temperatures at a single incident angle are assimilated into the Community Land Model (CLM) across Australia to improve soil moisture simulations. Therefore, the data assimilation system DasPy is coupled to the local ensemble transform Kalman filter (LETKF) as well as to the Community Microwave Emission Model (CMEM). Brightness temperature climatologies are precomputed to enable the assimilation of brightness temperature anomalies, making use of 6 years of SMOS data (2010)(2011)(2012)(2013)(2014)(2015). Mean correlation R with in situ measurements increases moderately from 0.61 to 0.68 (11 %) for upper soil layers if the root zone is included in the updates. A reduced improvement of 5 % is achieved if the assimilation is restricted to the upper soil layers. Root-zone simulations improve by 7 % when updating both the top layers and root zone, and by 4 % when only updating the top layers. Mean increments and increment standard deviations are compared for the experiments. The longterm assimilation impact is analysed by looking at a set of quantiles computed for soil moisture at each grid cell. Within hydrological monitoring systems, extreme dry or wet conditions are often defined via their relative occurrence, adding great importance to assimilation-induced quantile changes. Although still being limited now, longer L-band radiometer time series will become available and make model output improved by assimilating such data that are more usable for extreme event statistics.