Scientific/Technical/Management Section

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1 Scientific/Technical/Management Section 1. Results from prior research We are currently supported by two NASA grants (NASA/MAP project Global Land-Atmosphere-Ocean Interface Process Studies by Integrating the MERRA Reanalysis with Satellite and In situ Data", PI: Xubin Zeng, $791,561, 7/2014 6/2018; NASA/Terrestrial Ecology project Development of a High- Resolution Global Soil Depth Dataset, Co-PIs: Jon Pelletier and Xubin Zeng, $264,630, 7/2013-6/2015). Here we summarize only soil moisture-related results from these and other projects. Soil moisture measurements, analysis, and data assimilation: Developed a novel, non-contact method for measuring area-average soil moisture (SM) at the hectometer horizontal scale based on the measurement of low-energy cosmic-ray neutrons above the ground, and set up the COsmic-ray Soil Moisture Observing System (or the COSMOS) of more than 50 probes over the U.S., with similar networks coming into existence around the world (e.g., Zreda et al. 2012) Evaluated 22 precipitation and 20 SM products against high quality 58-year daily precipitation and July-September daily SM datasets over a 150 km 2 watershed in southeastern Arizona (Stillman et al. 2015) Evaluated model SM using the in situ SM data in U.S., China, and Brazil (Decker and Zeng 2009; Wang and Zeng 2012) Developed a revised covariance method to estimate SM in the deep layers based on observed SM in the top few centimeters (Zhang et al. 2010). Quantified the dependence of SM memory on soil depth (Wang et al. 2006) Evaluated the impact of SM and other environmental variables on the carbon cycle in Earth system models (Shao et al. 2013) Soil moisture-related land data development: Developed global 1 km and 8 km AVHRR and MODIS-based green vegetation fraction (Zeng et al. 2000, 2003; Miller et al. 2006; Broxton et al. 2014a) Developed global MODIS-based 0.5 km land cover type climatology (Broxton et al. 2014b) implemented in the Weather Research and Forecasting (WRF) model in 2014 Developed global MODIS-based 5 km maximum snow albedo data (Barlage et al. 2005) Developed the first global 1 km bedrock depth data (Brunke et al. 2015) Developed the vegetation type-dependent root distribution data (Zeng 2001) implemented in the ECMWF operational model, Community Land Model (CLM), and numerous other land models Soil moisture-related land model development: Coordinated the development of the initial version of CLM the land component of the Community Earth System Model (CESM) (Zeng et al. 2002; Dai et al. 2003). Developed the revised form of SM-based Richards equation (Zeng and Decker 2009) Developed an observationally-based formulation of soil ice fraction (Decker and Zeng 2006) Developed a computationally efficient hybrid 3-dimensional hydrological model for regional and global modeling (Hazenberg et al. 2015) 2. Introduction Importance of soil moisture. Soil moisture (SM) is highly variable in space and time (e.g., Seyfried and Wilcox, 1995; Brocca et al. 2012), impacting the structure, function and diversity of vegetation in drylands (Rodriguez-Iturbe et al. 1999). Spatial patterns of SM have been linked with soil type, vegetation, and topography (e.g., Lin et al. 2006). Furthermore, they change between dry and wet conditions. During dry conditions, SM patterns tend to be locally controlled and dominated by soil 1

2 texture, and during wet conditions non-local lateral transport results in increased connectivity and autocorrelation organized by topography (e.g., Robinson et al. 2012). Soil moisture plays a central role in the land-atmosphere exchange of energy, water, and carbon fluxes (Barré et al. 2008; Seneviratne et al. 2010; Koster et al. 2011). It influences the partitioning between infiltration and runoff during precipitation events. As such, it impacts climate and atmospheric processes, geomorphology, hydrology and biogeography (Legates et al. 2010). In addition, the ecosystem and carbon cycle are intimately linked to SM (Shao et al. 2013). Through its influence on evapotranspiration, SM modulates the surface energy balance, and thus the partitioning between latent and sensible fluxes at the land surface (e.g., Entekhabi et al. 1996). Numerous studies have emphasized the impact of SM, and more recently, groundwater, on the atmosphere (Beljaars et al. 1996; Lo and Famiglietti, 2011; Koirala et al. 2014). For instance, a proper accounting of the SM dynamics at the land surface has been shown to improve the predictability of atmospheric temperature and precipitation in the Northern Hemisphere, especially during the boreal summer (Guo et al. 2011). Based on Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations, Dirmeyer et al. (2013) showed that the impact of SM on the atmosphere is expected to increase for the future. Soil moisture measurements. Locally, SM can be observed by a variety of methods including manual collection and analysis of soil samples, automated point measurements of soil electrical properties (which change with soil water content), and low energy cosmic ray neutrons above the ground (whose intensity is inversely proportional to soil water content and other water on the surface; Zreda et al. 2012). However, the only method to measure SM globally is via satellite remote sensing. Until recently, soil wetness was estimated from data coming from instruments designed for other purposes (e.g. monitoring of snow cover). Early attempts to extract information about SM relied on detecting the secondary effects of SM on surface temperature via thermal sensors (e.g., Carlson et al. 1981). Because of the absorption and excitation properties of water in the microwave portion of the electromagnetic spectrum, these attempts have largely focused on using passive-microwave radiometers such as the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) (e.g., Gruhier et al. 2008), the Special Sensor Microwave Imager (SSM/I), and the Scanning Multichannel Microwave Radiometer (SMMR) (e.g., Paloscia et al. 2001). These radiometers, which operated in the C- and X- bands, could only provide information that is representative of the top ~1 cm of soil. Additionally, moderate vegetation with vegetation water content (VWC) of ~3 kg m -2 can attenuate the signal enough to prevent accurate measurement of SM. On the other hand SM measurements based on L-band emissions are representative of a thicker soil layer (~5 cm) as these emissions can penetrate through greater amounts of soil than can higher frequency microwaves (Kerr 2007). Similarly, they can penetrate through greater amounts of vegetation, and accurate SM measurements can be made for VWCs up to 5 kg m -2 (Entekhabi et al. 2010). More recently, Aquarius (since 2011) and Soil Moisture Ocean Salinity (SMOS) (since 2009) missions have provided SM products derived from passive L-band radiometers (Bindlish et al. 2015; Kerr et al. 2010). Aquarius and SMOS L-band SM products have relatively coarse spatial resolutions (~ km). Data at such resolutions, cannot account for small-scale heterogeneity in soil and land cover (Kerr 2007) and offer no sub-pixel scale variations in SM. The Soil Moisture Active-Passive (SMAP) mission uses both active and passive L-band microwave remote sensing to determine the land surface SM and freeze/thaw state at higher (3-9 km) resolution, and hence provides an unprecedented opportunity for process studies and land model evaluation and improvement (Entekhabi et al. 2010). SMAP products and bedrock depth. Soil moisture dynamics is strongly coupled to the groundwater (e.g. Ferguson and Maxwell 2010), and both SM and groundwater are closely tied to the thickness of the layer of soil and alluvium close to the Earth s surface. The thickness of this layer affects the water holding capacity of the soil, the rooting depth, and the transfer of energy between the surface and the deeper soil layers. Therefore, vegetation health, runoff potential, soil freeze and thaw, as well as land memory of previous climatic forcing could all be affected by the thickness of this layer. A number of previous observational and modeling studies have provided evidence for this link. For example, soil 2

3 depth has been shown to control infiltration rates in a desert basin in Nevada (Woolhiser et al. 2006), and accounting for shallow soil depths has been shown to increase the dynamic range of sensible and latent heat fluxes during summer (Gochis et al. 2009). Lawrence et al. (2008) demonstrated the importance of deep soil layers on the simulation of permafrost and soil ice. Recently, in recognition of the importance of representing soil thickness across the world, we have developed the first global 1 km dataset of soil and alluvial thickness (Pelletier et al. 2015, in preparation) under a NASA Terrestrial Ecology project (also see Section 1). Figure 1 (see Section 4) shows that this layer can range from 0-10s of meters or more. Such a dataset provides a crucial lower boundary condition in Land Surface Models (LSMs), which have used a constant value to represent soil thickness because such information has not been available globally (Brunke et al. 2015). This dataset was implemented into the CLM (Brunke et al. 2015) by modifying the model to accommodate the new dataset by (1) increasing the number of possible soil layers and allowing the number of soil layers to vary globally, (2) removing the separate treatment of the unconfined aquifer from that of SM, and (3) adjusting surface runoff and the rooting depth for shallow soils. Brunke et al. (2015) found that the modeled SM is most sensitive to soil thickness when the soil is shallow. In their simulations, the amplitudes of surface runoff and evapotranspiration and both the amplitude and timing of maximum baseflow changed in areas with shallow soil, compared to a control simulation with deep soil everywhere. Despite the demonstrated importance of soil and alluvial thickness data in a modeling framework (Brunke et al. 2015), such a link remains to be demonstrated, at large scale, using observational data. The SMAP mission is ideal for testing this potential link between surface SM and soil thickness because of its high spatial resolution (owing to its use of both active and passive L-band microwave remote sensing). This will allow for the best possible delineation between landscape units that have different soil thicknesses. Furthermore, SMAP includes a SM product for the rooting zone through data assimilation in a LSM, and soil thickness has an even stronger impact on the root zone SM than surface SM. Shallow soils can also limit rooting depths, potentially influencing rates of evapotranspiration. In addition, soil has different thermal properties than underlying saprolite/bedrock, so there may also be a link between soil depth and soil freeze/thaw. In the modeling study of Brunke et al. (2015), the mean annual range of soil temperature was reduced in areas where bedrock is deep, while the temperature range throughout the column was increased in areas where the bedrock is shallow, particularly in mountainous terrain. Objectives and scientific questions. Based on our new bedrock depth data (see Section 4) and our interdisciplinary experiences in SM measurements, analysis, data assimilation, and SM-related land data and model developments (see Section 1), our overarching goal is to use SMAP measurements and our variable bedrock depth data for land model evaluation and improvement. Four specific questions (corresponding to four tasks described in section 5) will be addressed: What is the spatiotemporal variability of SMAP data uncertainty? How does variable soil thickness affect the temporal variability of SMAP surface and root zone SM products over areas with shallow, intermediate, and deep bedrock depths? What is the spatiotemporal variability of land surface model deficiencies and how can SMAP measurements provide a unique perspective (e.g. for soil freeze/thaw state)? How is summer precipitation affected by antecedent SM based on satellite measurements and Earth system models? Section 3 addresses the relevance of our proposed work to NASA, while Section 4 describes the relevant satellite data and the Catchment LSM. Section 5 discusses four tasks that will be carried out to address the above four questions. Section 6 presents the work plan, management, and deliverables. 3. Relevance to NASA Earth Science The proposed work will directly address two of the three priorities in the Decadal Survey: Enabling advances in the study of the water, carbon, and energy cycles, especially on those topics that deal with the 3

4 intersections of these cycles and Exploring the impact of soil moisture variability and its role as the memory for the land surface, on weather and climate. The proposed work will focus on the theme of this solicitation Utilization of SMAP for model evaluation and improvement, and it will also contribute to another theme Utilization of SMAP products for process studies. Furthermore, this project will contribute to two of the three priorities in this solicitation Enabling advances in the study of the water, carbon, and energy cycles, especially on those topics that deal with the intersections of these cycles and exploring the impact of soil moisture variability, and its role as the memory for the land surface, on weather and climate. Extensive NASA satellite products (e.g., SMAP, GPM, and Aquarius) will be used. 4. Gridded data and Catchment LSM descriptions SMAP data. The SMAP sensor measures surface (< 5 cm) SM using both an L-band radar and an L- band radiometer. The combination of the lower resolution radiometer measurements and the higher resolution radar measurements allows SMAP to measure SM at an unprecedented resolution (~3-30 km). SMAP also offers an advantage over other previous and current generation microwave sensors of mitigating the effects of anthropogenic Radio Frequency Interference through the use of selective filters and an adjustable carrier frequency. The SMAP platform is conically scanning with a constant incidence angle so as to reduce the effects of vegetation and surface roughness. The 40º incidence angle allows SMAP to obtain global coverage in two to three days (Entekhabi et al. 2010). SMAP s passive microwave measurements of brightness temperature are used to provide fairly accurate estimates of SM. On the other hand, the radar backscatter achieves higher resolution for offnadir views but is relatively less accurate at measuring SM than the radiometer, especially in the presence of dense vegetation. Hence, the combination of the two measurements provides an optimal balance of resolution and accuracy of near surface (<5 cm) SM. In this project, we will use the coarse resolution (36 km) L2/3_SM_P products determined from the radiometer brightness temperatures (O Neill et al. 2014), the combined 9 km L2/3_SM_AP products (Entekhabi et al. 2014), and the high resolution (3 km) L2/3_SM_A product derived from radar backscatter (Kim et al. 2014). In addition to SM measurements derived from satellite radiances, SMAP satellite data will also be assimilated into the Goddard Modeling and Assimilation Office (GMAO) Catchment LSM (Koster et al. 2000). This merged product (L4_SM), providing global surface and root zone SM products that are spatio-temporally complete (Reichle et al. 2014), will also be used in our project. In addition, we will use the SMAP freeze/thaw state of the land surface at 3 km resolution in the L3_FT_A product. In the northern high latitudes (north of 45 N latitude), measurements will be recorded for both ascending and descending overpasses providing estimates of frozen, unfrozen, or transition states. Such measurements rely on changes in surface dielectric properties that occur when water transitions between the solid and the liquid phases, and the soil freeze/thaw state will be classified based on the temporally varying radar backscatter measurements in terms of reference radar backscatter measurements of the thawed and frozen states for each pixel (Dunbar et al. 2014). The SMAP SM data is provided only when the freeze/thaw (F/T) state is thawed or partially frozen. Other satellite-based soil moisture data. Besides the SMAP SM data, we will also use the SM products from SMOS and Aquarius L-band radiometers for intercomparison. We have evaluated these products recently using in situ measurements (Stillman et al. 2015). TRMM/GPM precipitation data. The Tropical Rainfall Measuring Mission (TRMM) was designed to improve our understanding of the distribution and variability of precipitation within the tropics. TRMM contains both a precipitation radar (PR) and microwave imager (TMI) to measure precipitation. TRMM was launched in 1997 and delivered a 3-hourly 0.25 x 0.25 dataset covering 50 N-S in latitude, by combining TRMM satellite data with other precipitation products (Huffman et al. 2007). As a successor to TRMM, the Global Precipitation Measurement (GPM) mission includes the core observatory satellite launched in February 2014 and a constellation of sun-synchronous orbiting satellites. The core observatory contains both a dual-frequency precipitation radar (DPR) and a 4

5 microwave imager (GMI) to obtain precipitation information. We will use the GPM level hourly 0.1 x 0.1 IMERG (Integrated Multi-satellitE Retrievals for GPM) global product (Smith et al. 2007). Soil depth/vegetation data. In addition to the satellite data listed above, we will use our global soil and alluvial thickness (Pelletier et al. 2015, in preparation; Figure 1). This dataset utilized different approaches to separately estimate soil and alluvial thickness for upland hillslopes, upland valley bottoms, and lowlands, as the character of soil depth is fundamentally different for these units: upland hillslopes have relatively shallow soils (~1 m), while valleys and lowlands have relatively deeper soils/alluvium (10s of meters or more) (Fig. 1). These three units are distinguished at the 90 m/pixel scale using a valley network extraction algorithm as well as criteria related to geologic age. Figure 1: Global map of soil and alluvial thickness. We will also make use of our global 0.5 km land cover type data (Broxton et al., 2014b). This dataset is based on 10 years of Moderate Resolution Imaging Spectroradiometer (MODIS) data. The original MODIS data was found to have a significant amount of spurious interannual variability, and data from was combined using confidence scores for each land cover classification to create a value-added product that is more accurate than the original data (Broxton et al., 2014b). Finally, we will use our global maximum green vegetation fraction data (MGVF; Broxton et al., 2014a). This dataset is based on 12 years of MODIS data and its derivation is based on the widely used methodology of Zeng et al. (2000). The same approach was also used to derive seasonally variable green vegetation fraction (GVF) in our prior work (Miller et al. 2006, Scheftic et al. 2014). The Catchment land surface model. We will pay particular attention to the evaluation and improvement of GMAO s Catchment land surface model (LSM) (Koster et al. 2000, Ducharne et al. 2000), as it is used to produce the SMAP L4_SM product (Reichle et al. 2014) and the GMAO Modern Era Retrospective- Analysis for Research and Applications (MERRA) reanalysis (Rienecker et al. 2011). Instead of operating on fixed grid boxes like most LSMs, the Catchment LSM operates on hydrological catchments (Koster et al. 2000). Sub-catchment variability in SM and water table depth is determined by dividing the area of each catchment into three hydrological regimes: (1) the area that is completely saturated along riverbeds, (2) a transitional area in which the soil is unsaturated but transpiration is unstressed, and (3) the area in which the soil is too dry for transpiration to take place. Thus, runoff and evaporation are controlled differently in each of these regions (Koster et al. 2000). Soil moisture values prognosticated by the Catchment LSM include an area-integrated catchment deficit, i.e., the average amount of water per unit area required to bring the entire soil column throughout the catchment to saturation, a root zone excess describing how much the root zone moisture is out of balance from equilibrium, and a surface excess describing how much surface moisture is out of balance from the root zone value (Koster et al. 2000, Rienecker et al. 2008, Reichle et al. 2014). For L4_SM, the surface layer is defined to be the top 5 cm consistent with what is provided by the L2_SM_AP product, and the root zone is considered to be the top 1 m (Reichle et al. 2014). 5

6 5. Proposed work We will carry out four tasks here to address the four scientific questions in Section 2. Task 1 of SMAP data evaluation (Section 5.1) is the basis for the other three tasks. Task 2 (Section 5.2) will explore the impacts of both precipitation and variable bedrock depth on the temporal variability of SM. Building on these two tasks, Task 3 (Section 5.3) will use SMAP data to evaluate land data assimilation systems and land models. Task 4 (Section 5.4) will document the linkage of summer precipitation to antecedent SM using SMAP and GPM data and use the observed relationship to evaluate earth system models. Before we start these tasks, we will explore all data fields of SMAP products. For instance, for the combined 9 km L2/3_SM_AP product (Entekhabi et al. 2014), the data group for each grid cell includes 56 fields. Studying these fields carefully will help us to better understand the strengths and weaknesses of the SMAP products and potential scientific uses of the quality information (as was done in the development of our value-added global 0.5 km land cover type climatology data ; Broxton et al. 2014b). 5.1 SMAP data evaluation using in situ soil moisture data (Task 1) Numerous in situ SM data are available for the evaluation and validation of the SMAP products. Many methods measure SM at a point (Robinson et al. 2008). However, point measurements share a critical shortcoming: they are not representative of the surrounding area because SM is spatially heterogeneous over a range of length scales (e.g., Entin et al. 2000; Famiglietti et al. 2008). Such heterogeneity precludes meaningful assessment of area-representative SM (e.g., from SMAP) from a single point or a single profile (Loew and Schlenz 2011; Zreda et al. 2012). While the data that we will use in this study will also be used for core validation or are being considered for validation of SMAP data (Jackson et al. 2012), our strategy in Task 1 is to make innovative use of the data that represent area averages based on our previous experiences. Walnut Gulch Experimental Watershed (WGEW) Data. The WGEW of 150 km 2 is located in southeastern Arizona. The USDA Agricultural Research Service (ARS) has installed and maintained 19 in situ SM capacitance probes at a depth of 5 cm, 88 raingauges, and other meteorological instruments. These have been measuring half-hourly SM since 2002 (Goodrich et al., 2008). Additionally, Stillman et al. (2014) provided observationally constrained July-September SM data from 1956-present at 88 stations over WGEW. This product infers SM from precipitation gauge data (88 gauges installed beginning in 1953) and is calibrated using the 19 in situ SM probes. We have recently evaluated 22 precipitation products and 20 SM products using the in situ daily data over WGEW. We have also evaluated the dependence of grid box averaged precipitation on grid sizes from 0.1 o to 2.5 o using high resolution precipitation data (Stillman et al. 2015). Figure 2 evaluates 20 SM products using four metrics: correlation (R), mean bias (BIAS), bias corrected root mean square difference (NRMSD), and the standard deviation normalized by the in situ data ( ). Overall, satellite data perform best for two metrics (BIAS and NRMSD). None of the satellite products have long enough data records (all have fewer than 5 years of data) for computing the other two metrics. NASA/GMAO reanalysis MERRA (12) performs best out of the reanalysis products, with the lowest mean bias and better than average R and NRMSD. In Task 1, we will repeat the above analysis over WGEW by including the SMAP SM data along with other data used in Stillman et al. (2015). In particular, the relative performance of SMAP data versus Aquarius and SMOS L-band products (1 and 3 in Fig. 2) will be of much interest to the community. Besides these satellite products, the NASA MERRA reanalysis data (Rienecker et al. 2011) (product 12 in Fig. 2) will also be emphasized because MERRA includes the same Catchment LSM used in the land data assimilation for SMAP L4_SM product (including root zone SM) which is forced using MERRA meteorological conditions. MERRA data are available from 1979 to present. Two-dimensional fields (typically surface and top of the atmosphere fields) are available from MERRA at the model native horizontal resolution of 1/2 latitude 2/3 longitude and at hourly intervals. 6

7 Figure 2: Performance statistics of each monthly summer (July- September) SM product against SM data over WGEW. R and are shown only for products with at least 10 years of data. Products 1-3 are satellite based (Aquarius, AMSR-E, and SMOS, respectively), products 4-11 are Earth system model outputs, products are reanalysis data [including MERRA (12) and MERRA-Land (13)], and products 19 and 20 are global and North American land data assimilation systems, respectively. MERRA s deficiencies in reproducing the amount and intensity of precipitation with respect to observation-based datasets prompted the development of MERRA-Land (Reichle et al. 2011; product 13 in Fig. 2). MERRA-Land uses identical forcing to MERRA except that the precipitation forcing is corrected using observed precipitation product. The Catchment LSM (Koster et al. 2000) was also improved in MERRA-Land. Besides the above in situ data, there will be a multi-institution field campaign SMAPVEX15 over WGEW and surrounding areas for the calibration and validation of SMAP SM and GPM precipitation measurements in summer Our team will participate in the field campaign, and will also use the in situ SM data for SMAP data evaluation in Task 1. COSMOS soil moisture data. Under a prior NSF project, we have developed and implemented the cosmic-ray method for measuring area-average SM at the hectometer horizontal scale in the COsmic-ray Soil Moisture Observing System (or the COSMOS; Zreda et al. 2012). The stationary cosmic-ray SM probe measures the neutrons that are generated by cosmic rays within air, soil, and other materials. These cosmic rays are primarily moderated by hydrogen atoms in soil water, and emitted to the atmosphere where they mix instantaneously at a scale of hundreds of meters. The density of cosmic rays that is measured is inversely correlated with SM. The COSMOS has already deployed more than 50 cosmic-ray probes, distributed mainly in the USA, each generating a time series of average SM over its hectare-scale footprint, with similar networks coming into existence around the world: The horizontal effective measurement area of COSMOS probes is near-constant and approximately 300m in radius at sea level (Desilets and Zreda 2013). It depends on the chemical and physical properties of the atmosphere, and is nearly independent of SM content. Because it represents area average SM, the COSMOS data are much more representative (than in situ point measurements) of SMAP and other satellite SM products, even though the scale of COSMOS data is still smaller than that of the satellite data. A more serious issue is that the effective depth of COSMOS measurement depends strongly on SM. It decreases non-linearly from ~70 cm in dry soils (with zero water content) to ~10 cm in saturated soils (0.40m 3 m 3 ) (Zreda et al. 2008). In other words, there is a mismatch between the COSMOS data (in the top cm of soil) versus satellite data (in the top few centimeters of soil). In Task 1, we will use the international COSMOS network for the SMAP, SMOS, and Aquarius product evaluations in two ways. First, we will directly compare the temporal variation of SM from 7

8 COSMOS network and SMAP SM in the top 5 cm and in the rooting zone. Even though the absolute values may be quantitatively different between the COSMOS and satellite data, the temporal variation of SM should be similar. Second, we will assimilate the COSMOS data through the ensemble data assimilation method (Anderson 2009) based on the Noah LSM (Ek et al. 2003), as we did in Rosolem et al. (2014). Relying on previous work here ensures that this sub-task can be done efficiently. To be fully consistent with SMAP L4_SM product, the Catchment LSM should be used along with the ensemble Kalman filter (Reichle et al. 2014) for COSMOS data assimilation. However, this would take much more efforts than we have (considering all other tasks) and hence will not be pursued in this project. Flux tower data. A global flux tower network (FLUXNET) has been developed for the measurements of land surface radiative and turbulent fluxes, SM (usually in the top 10 cm of soil), and other quantities (Baldocchi et al. 2001). The locations and information of more than 100 towers are available at: This network covers North America, Europe, Asia, Africa, South America, and Australia. It also covers different land cover types, from shrubland, grassland, to forests. We emphasize the FLUXNET datasets for two reasons: they provide comprehensive below- and above-ground measurements that will help interpret the SM measurements, and we have used these data extensively in our prior studies. For instance, Decker et al. (2012) used the air temperature, wind speed, downward solar radiation, net total radiation, latent heat flux, and sensible heat flux data from 33 different towers in North America to evaluate five reanalysis products (including MERRA). They also discussed the uncertainties due to measurement error and scale differences. In Task 1, we will use these in situ SM data for the evaluation of SMAP (as well as SMOS, and Aquarius) products in two ways. First, recognizing the large scale mismatch, we will focus on the temporal variation of SM (using the three metrics of R, NRMSD, and ), rather than the mean bias. Second, because FLUXNET covers all land cover types and contains other data that are relevant to SM dynamics (e.g. radiation), we will use these data to assess the dependence of the SMAP SM uncertainties on land cover type and green vegetation fraction (GVF). Even though the SMAP volumetric SM accuracy is estimated to be ±0.04 m 3 m -3, its actual accuracy in global applications is still uncertain due to a variety of factors (e.g., due to uncertainties of algorithm parameters or ancillary data). For instance, current SMAP algorithms implicitly assume full canopy cover in each pixel because a single value of vegetation water content (VWC) (within and/or above canopy), which depends on the vegetation volume, is used in the retrieval. Realistically, vegetation only completely covers an entire pixel in the wettest regions. For a pixel over a semiarid region with a 50% maximum GVF and VWC = 1 kg m -2, SM can be retrieved by treating the pixel together or by treating it as two sub-pixels (bare soil sub-pixel with zero VWC and vegetated sub-pixel with VWC = 2 kg m -2 ). Because an exponential function of VWC is used in the retrieval (e.g., Njoku et al. 2003), different results are expected for the two situations below. While evaluating the errors in the SMAP SM in Task 1, we will consider their dependence on GVF for each land cover type. If there is a consistent pattern in SM errors according to GVF, we will interact with the SMAP team for the revision of the SMAP retrieval algorithm. In these analyses, we will use the GVF and land cover data developed in our prior work (Zeng et al. 2000; Miller et al. 2006; Scheftic et al. 2014; Broxton et al. 2014a,b) and mentioned in Section 4. Another uncertainty of the SMAP SM is related to the MODIS land cover type data. The values of several retrieval algorithm parameters and the relationship between VWC and the Normalized Difference Vegetation Index (NDVI) are all dependent upon land cover types (O Neill et al. 2014). We have recently found that the MODIS land cover product has an unnatural interannual variability with 40% of land pixels globally having land cover type change one or more times during the period (Broxton et al. 2014b). In Task 1, we will compare the land cover data used in the SMAP retrieval with our land cover climatology dataset (Broxton et al. 2014b), and evaluate if and how this affects the performance of the SMAP SM product in comparison with the FLUXNET in situ data. We will then interact with the SMAP team on the potential use of our value-added MODIS-derived land cover climatology (Broxton et al. 2014b). 8

9 5.2 Impact of variable bedrock depth on the temporal variability of SMAP soil moisture (Task 2) The temporal variability of SM is primarily driven by precipitation. Precipitation events increase SM, which subsequently decreases over time due to evapotranspiration and runoff. Similarly, a negative anomaly (i.e., a drought) can persist over time if there is not much precipitation (Rahman et al. 2015). The SM memory has been widely recognized because of the role of land as a low pass filter of precipitation temporal variability (e.g., Delworth and Manabe 1988; Koster and Suarez 2001; Seneviratne and Koster 2012). In our prior study (Wang et al. 2006), we considered the response of three layers (the canopy, surface, and root zone), rather than a single bucket as in many previous studies, to high-frequency precipitation events, and found that surface SM responds very quickly to precipitation, while the root zone SM has a longer memory. Over the last decade, numerous studies have also identified the impact of groundwater on both SM and atmospheric processes, which depend on the moisture and energy availability in a watershed (e.g., Ferguson and Maxwell 2010). At short timescales, the exchange between groundwater and SM under (energy limited) direct runoff conditions is much larger than that under (moisture limited) baseflow conditions. At monthly to seasonal timescales, the location of the groundwater can have a positive impact on the SM supply and the evapotranspiration to the atmosphere. For instance, Lo and Famiglietti (2010) used the Community Land Model (CLM) to demonstrate that capillary fluxes mainly affect root zone SM for intermediate groundwater depths (1.6-3m). At the global scale, incorporation of groundwater dynamics in global simulations generally increases SM, evapotranspiration and cloud cover fraction, and decreases surface air temperature (Lo and Famiglietti, 2011; Koirala et al. 2014). Changes in the bottom boundary condition of a global climate model have also been shown to have an impact in other land surface models (Campoy et al. 2013). Groundwater is closely related to the bedrock depth. As mentioned earlier, we have developed the global 1 km soil thickness dataset (Pelletier et al., in preparation; Fig. 1). Furthermore, we have assessed the impact of variable soil thickness on SM at different depths based on global offline CLM modeling (Brunke et al. 2015). The assumption of constant soil thickness (due to the lack of global estimates of bedrock depth) is one of the recognized weaknesses of LSMs as used in weather and climate models. Including variable soil thickness affects the simulations most in regions with shallow bedrock corresponding predominantly to areas of mountainous terrain (Brunke et al. 2015). The greatest changes are to baseflow, with the annual maximum generally occurring earlier. These changes are tied to SM changes and are most substantial in locations with shallow bedrock. It was also found that even the total water storage anomalies substantially differ for a river basin with more mountainous terrain. As an example, our global 1 km bedrock depth data shows a substantial spatial variation within a CLM grid box of 0.9 latitude by 1.25 longitude in Colorado. Figure 3 shows that the mean annual cycles in surface (for the top two CLM layers down to 4.5 cm) and root zone (for the top six layers down to 0.8 m) volumetric SM for this grid box are quite different in the early part of the year using a soil thickness of five layers (down to ~0.3 m) vs. ten layers (~3.8 m). The mean root zone SM in the CLM simulation with a 5-layer soil thickness is well above one standard deviation of the 10-layer soil thickness run at the March maximum. The shallower soil simulation has a greater seasonal cycle of root zone SM (Fig. 3b). The seasonal amplitude of surface SM is not much affected by the soil depth, but the surface SM maximum (in February) for the deeper soil simulation is delayed by a month in the shallow soil simulation (March; Fig. 3a). After evaluating SMAP surface and root-zone SM products in Task 1, we will quantify the impact of both precipitation (from GPM) and soil thickness (from our global dataset) on the temporal variability of SMAP SM in Task 2. We will use both the surface and root zone SM from SMAP s L4_SM product. For grid cells with similar temporal variability of GPM precipitation over a region, we will analyze the memory of surface and root zone SM following our prior study (Wang et al. 2006). The variability of such memories will then be related to the variability of bedrock depth over the same region. 9

10 This analysis will then be repeated over different continents. Figure 3. The mean annual cycles (solid lines) in (a) surface and (b) root zone volumetric SM (in m 3 m -3 ) for single column runs over Colorado using the same atmospheric forcing for soil thicknesses of five (orange; ~0.3 m) and ten layers (light blue; ~3.8 m). Dotted lines represent the one standard deviation of the interannual variability of monthly SM. For grid cells with similar bedrock depth over a region, we will also analyze the relationship of surface and root zone SM memories with precipitation (Koster and Suarez 1996; Wang et al. 2006). This analysis will then be repeated over different continents. For these regions, we will also repeat the SMAP ensemble Kalman filter data assimilation using the Catchment LSM (Reichle et al. 2014) along with our bedrock depth data in Task 2, following our approach (using CLM) in Brunke et al. (2015). Then the surface and root zone SM data will be compared with the SMAP L4_SM data. For regions with large differences, in situ data from Section 5.1 (Task 1) will be used to assess the potential improvement of SM due to the use of our bedrock depth data. Through these efforts, we will then pass both the bedrock depth data and the approach to implement the dataset in the Catchment LSM to the developers of the SMAP L4_SM data. 5.3 Using SMAP data to evaluate GLDAS (Task 3) With the evaluation and understanding of SMAP data from Tasks 1 and 2, here we will use the SMAP SM data to evaluate widely-used SM products from the global land data assimilation system version 2 (GLDAS-2; Rodell et al. 2004). The forcing data is derived from observed precipitation and radiation datasets, as well as other data from atmospheric reanalysis. GLDAS-2 drives four LSMs: Catchment (Koster et al. 2000), Noah (Ek et al. 2003), CLM (Lawrence et al. 2011), and VIC (Liang et al. 1994) through the Land Information System (Kumar et al., 2006). GLDAS-2 data are available at 0.25 and 1 resolution. MERRA-Land product will also be included in this analysis due to its use of precipitation measurements in driving the Catchment LSM, as mentioned earlier. Sabater et al. (2007) showed considerable differences between L-band brightness temperature derived surface SM estimates and those simulated by a LSM for a single fallow ground area. We have also done preliminary global comparison between satellite-derived SM products and GLDAS products. For example, Figure 4 shows the difference between GLDAS-2/Noah and the European Space Agency s merged SM product (CCI SM v02.1; Liu et al. 2011, 2012; Wagner et al. 2012) in terms of the median and the interquartile range (the 75 th percentile minus the 25 th percentile) of summertime surface SM from The satellite-derived values of SM are, on average, lower than is depicted in the Noah LSM over higher latitudes, while the opposite is true in some arid regions (especially in Africa and Australia) (Fig. 4c). The interannual variability of the satellite derived SM values is larger than the Noah results in many areas in the lower latitudes (Fig. 4d). 10

11 Figure 4. June-August GLDAS-2/Noah and ESA satellite-derived surface SM based on data from Panels a) and b) show the median and interquartile range (Q75-Q25) of SM for the ESA product, and Panels c) and d) show the difference between the ESA product and the top level SM from GLDAS2/Noah. In Task 3, we will do similar comparisons between the SMAP data and various GLDAS products (including MERRA-Land). There are likely to be significant differences between SMAP and the GLDAS models. Some of these differences might be related to bedrock depth. The superior spatial resolution of the SMAP data will be able to better differentiate between SM for different terrain types (e.g. mountains vs. lowlands) with different bedrock depths. We will re-run GLDAS experiments using CLM and Catchment LSMs with variable bedrock depth. We have implemented and tested our bedrock depth data in CLM (Brunke et al. 2015), while the implementation and testing of our data in Catchment will be done in Task 2. The sensitivity of the results to the use of variable bedrock depth data will be emphasized. Another reason for the differences could be LSM deficiencies. Our global comparison efforts in Task 3 are also expected to help identify such LSM deficiencies along with possible solutions, as has been demonstrated extensively in our prior work (e.g., Zeng and Decker 2009; Wang et al. 2010). Freeze/thaw (F/T) state is another primary product of SMAP, and it will also be a major focus of these model evaluations. This product uses a seasonal threshold approach that examines how radar backscatter measurements relate to seasonal reference frozen and thawed states (Dunbar et al. 2014). A similar method was also utilized to obtain F/T state based on microwave brightness temperatures from SSMR and SSM/I (Kim et al. 2011). While this product is valuable for high-latitude monitoring (e.g., Kimball et al. 2001), its comparison to LSMs (particularly those explicitly considering soil water and soil ice) is not straightforward. We have done preliminary work on such comparisons under our DOE project on the development of the Regional Arctic System Model (RASM; Maslowski et al. 2015, in preparation). As an example, Figure 5 compares the mean day of the year (DOY) of thaw and freeze as derived from the SSMR-SSM/I F/T product (Kim et al. 2011) versus those from RASM based on surface and soil temperature. Modeled thaw and freeze occurs much earlier than in the satellite-derived product. In Task 3, we will do similar comparisons between the SMAP F/T data and various GLDAS-2 products (including MERRA-Land). For regions with large differences, we will use the in situ data in Section 5.1 (Task 1) to further understand such differences. In some land models, soil water is not allowed to exist when the soil temperature is below the freezing point. Our analysis of observational in situ data over Alaska and other regions indicates, however, this is not the case (Decker and Zeng 2006). The co-existence of water and ice in soils, 11

12 particularly when soils are dry, add a further complication to SMAP s binary distinction between frozen and thawed states. Figure 5. A comparison of the day of year (DOY) of thaw and freeze as derived from SSMR and SSM/I brightness temperatures and from the Regional Arctic System Model (RASM) averaged from We have come up with a new formulation based on theoretical considerations and in situ data to account for the existence of liquid water in the soil when the temperature is below the freezing point (Decker and Zeng 2006). The new formulation matches observational data better than other approaches used by various LSMs (including Noah). This affects several land surface processes (e.g., movement of water in the soil; availability of soil water for transpiration in winter; latent heat release associated with phase change). In addition, the new soil ice formation was found to affect the CLM modeling of soil water content and soil temperature. In Task 3, we will also evaluate the connection between the SMAP F/T product and the soil ice formulations in land models. The question is: what is the exact (quantitative) meaning of the SMAP F/T state product and how does it relate to existing knowledge and modeling of soil ice? In particular, we will test how well the SMAP binary F/T classification relates to the ice fraction model data under various antecedent SM conditions. A similar analysis, which relates SMAP F/T data to antecedent SM and soil temperature data (from the L4_SM products), can be compared to in-situ observations such as those used in Decker and Zeng (2006) for further verification. In particular, as SM probes usually measure soil water only, rather than the total soil water and ice (or just soil ice) (Decker and Zeng 2006), in Task 3 we will explore the use of the SMAP normalized difference radar backscatter index for soil ice fraction (Dunbar et al. 2014) for instance, for the further development of the formulation in Decker and Zeng (2006). This index is computed as [ (t) - ( fr) ]/[ (th) - ( fr) ] where (t) is the radar backscatter measurement at time t, and ( fr) and (th) are the corresponding values at frozen and thawed reference states (Dunbar et al. 2014). We will also implement the soil ice formulation in the Catchment LSM in Task 3, and assess its impact on the GLDAS/Catchment SM simulations during the freeze/thaw periods. These tests will be the basis for the future implementation of soil ice formulation in Catchment (e.g., for future MERRA and MERRA-Land reanalysis, and data assimilation for the SMAP L4_SM products). 12

13 5.4 Linking summer precipitation to antecedent soil moisture (Task 4) Besides ocean processes, land processes (such as SM, vegetation, and snowpack) affect atmospheric processes on weekly, seasonal, and even longer time scales (Koster et al. 2004; Notaro et al. 2006; Dirmeyer et al. 2009; Jaeger and Seneviratne 2011). Furthermore, the land s impact on temperature tends to be stronger than on precipitation (Seneviratne et al. 2013). Koster et al. (2004) showed that hot spots for SM anomalies affecting precipitation generally are in the transition zones between wet and dry climates. For the Northern Hemisphere during the boreal summer, Zhang et al. (2008) showed that these hot spots mainly appear in arid to semi-arid climatic transitions zones or in semi-humid forest to grassland transition zones, including the southwestern U.S. Within these regions, antecedent SM anomalies affect precipitation anomalies. To estimate the land precipitation coupling strength, we proposed a new parameter computed as the ratio of the covariance between monthly or seasonal precipitation and evaporation anomalies (from their climatological means) over the variance of precipitation anomalies (Zeng et al. 2010). This parameter is easy to compute and is insensitive to the horizontal scales used; however, it does not provide causality. A relatively high value is a necessary but not sufficient condition for a relatively strong land precipitation coupling. The strongest coupling (i.e., hot spots) occurs over the western and central parts of North America, part of the Eurasian mid-latitudes, and the Sahel in boreal summer and over most of Australia, Argentina, and South Africa in austral summer. There are a plethora of studies that show that at diurnal time scales, SM anomalies affect the partitioning of net radiative fluxes into sensible, latent, and ground heat fluxes. Santanello et al. (2009, 2011) developed a local land-atmosphere coupling framework to diagnose the contributions from surface energy fluxes and entrainment from aloft. Chen and Avissar (1994) showed that SM had a significant impact on the timing of the onset of clouds and the intensity and distribution of precipitation. Interactions between SM and shallow cumulus generation are highly non-linear, but the impact of SM tends to be stronger for drier atmospheric conditions (Chen and Avissar 1994). Findell and Eltahir (2003a,b) showed that SM anomalies can either enhance or suppress convective precipitation. Using reanalysis data, Findell et al. (2011) showed that increases in evaporation lead to a larger probability of afternoon precipitation in the eastern U.S. and Mexico, but does not affect the precipitation intensity. On the basis of satellite precipitation data, Taylor et al. (2012) found that afternoon precipitation is more likely over drier soils at the global scale. Guillod et al. (2014) showed a strong positive feedback between the SM and the atmosphere in the southwestern U.S., and a much weaker feedback in the eastern U.S. As was shown in Task 2, soil moisture feedbacks have also been identified through the incorporation of groundwater dynamics in global modeling studies as groundwater has a direct impact on root zone SM. Lo and Famiglietti (2011) showed two different types of positive SM-atmosphere feedbacks. In the wet tropics, higher SM values interacting with the groundwater, lead to a positive precipitation feedback in the ascending branch of the Hadley circulation and reduces precipitation in the descending branch. For the transition zone between wet and dry climates, the positive feedback of SM on precipitation is more local (Lo and Famiglietti 2011; Wei and Dirmeyer 2012; Guo and Dirmeyer 2013). In Task 4, we will explore the linkage of summer precipitation to antecedent SM using the SMAP SM and GPM precipitation (described in Section 4). Our key strategy is to address this issue at multiple temporal scales. As an example, Figure 6 shows the temporal variation of in situ and satellite SM and precipitation data at seasonal, weekly, and daily time scales over WGEW. At the local scale, we will make use of the SM and precipitation data over WGEW from in situ measurements (Stillman et al. 2014, 2015), SMAP, and GPM. This area is also part of the North American monsoon region where both tropical moisture and precipitation recycling play important roles in supporting monsoon rainfall in summer (Schmitz and Mullen 1996; Schiffer and Nesbitt 2012; Hu and Dominguez 2015). In particular, as part of the ground validation for SMAP, the 2015 SMAP Validation Experiment (SMAPVEX15) will take place this summer over WGEW and surrounding areas. During SMAPVEX15, multiple airborne sensors will be deployed to characterize fine-resolution SM patterns within individual 13

14 SMAP radar pixels (~3 km). These observations will be compared to ground-based SM measurements obtained from ground sampling and permanent in situ SM instrumentation. Figure 6. Seasonal (left), weekly (middle) and daily (right) variation in SM and precipitation over WGEW, including in situ surface SM (black lines: average; gray areas: 10th to 90th percentiles), SMOS SM (red lines), in situ mean precipitation (green lines), and GPM-IMERG precipitation product (blue lines). The periods between the two vertical dash lines in panels a) and b) are shown in panels b) and c), respectively. As part of SMAPVEX15, Co-I Hazenberg has applied for a NASA Rapid Response to Earth System Events proposal to install and operate six GPS receivers over the SMAPVEX15 domain. These GPS sites are in addition to seven permanent GPS-Met sites funded by the University of Arizona throughout southeastern Arizona at a range of elevations, with disdrometers at three of these locations also spanning a range of elevations. Furthermore, in Task 4 we will analyze the in situ and satellite SM and precipitation data over the Southern Great Plains (SGP) where comprehensive land surface and atmospheric measurements are available through the DOE Atmospheric Radiation Measurement (ARM) Program ( Our analysis of the data over SGP and WGEW will allow us to study land-atmosphere coupling in two regions with different diurnal cycles of land-atmosphere interactions. Besides the afternoon convection, there is a well-known nocturnal precipitation peak over the SGP due to the low level jet. In contrast, Southern Arizona has an afternoon precipitation peak associated with the North American monsoon. Southern Arizona also has more complex terrain than the relatively flat SGP. In Task 4, we will also do a more detailed analysis of the coupling between SM, atmospheric boundary layer, clouds, and precipitation over SGP due to the availability of such data from the ARM Program and over WGEW during SMAPVEX15. For instance, a given SM anomaly may produce a positive or a negative response in the precipitation, depending on the atmospheric profile (Findell and Eltahir 2003a,b): wet soils may increase surface evapotranspiration and hence moisten the boundary layer, while decreased sensible heat flux may slow down the daytime boundary layer growth. How these processes compete to affect convection will be emphasized. It will also be interesting to see if the mechanisms are the same or different from each other in these two areas. Then in Task 4 we will expand to the global analysis of the link of summer precipitation to antecedent SM at different temporal scales: diurnal, weekly, and seasonal (as illustrated in Fig. 6). These results will then be compared with previous work (including our prior work in Zeng et al. 2010) and linked with bedrock depth through our findings in Task 2. Through these efforts, we will be able to identify regions where a time-delayed relation between SM and precipitation exists. Furthermore, we will use these results to evaluate the SM-precipitation relationship (rather than actual SM and precipitation values) from CMIP5 Earth system models in Task 4. We have done extensive analysis of CMIP5 models in our prior research, including the use of 48 CMIP models in our most recent study on the spurious numerical oscillations near steep topographies in these models (Geil and Zeng 2015). In these model-data comparisons, we will focus on both the magnitude (coupling strength) and the sign (positive or negative feedback). For instance, can CMIP5 models show some of the negative feedbacks of SM to local precipitation? In fact, simulated SM-precipitation feedback mechanism has been shown to depend on both grid size and the subgrid parameterization schemes 14