Multiscale assimilation of Advanced Microwave Scanning Radiometer-EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado

Eight years (2002–2010) of Advanced Microwave Scanning Radiometer–EOS (AMSR-E) snow water equivalent (SWE) retrievals and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) observations are assimilated separately or jointly into the Noah land surface model over a domain in Northern Colorado. A multiscale ensemble Kalman filter (EnKF) is used, supplemented with a rule-based update. The satellite data are either left unscaled or are scaled for anomaly assimilation. The results are validated against in situ observations at 14 high-elevation Snowpack Telemetry (SNOTEL) sites with typically deep snow and at 4 lower-elevation Cooperative Observer Program (COOP) sites. Assimilation of coarse-scale AMSR-E SWE and fine-scale MODIS SCF observations both result in realistic spatial SWE patterns. At COOP sites with shallow snowpacks, AMSR-E SWE and MODIS SCF data assimilation are beneficial separately, and joint SWE and SCF assimilation yields significantly improved root-mean-square error and correlation values for scaled and unscaled data assimilation. In areas of deep snow where the SNOTEL sites are located, however, AMSR-E retrievals are typically biased low and assimilation without prior scaling leads to degraded SWE estimates. Anomaly SWE assimilation could not improve the interannual SWE variations in the assimilation results because the AMSR-E retrievals lack realistic interannual variability in deep snowpacks. SCF assimilation has only a marginal impact at the SNOTEL locations because these sites experience extended periods of near-complete snow cover. Across all sites, SCF assimilation improves the timing of the onset of the snow season but without a net improvement of SWE amounts

Surface Hydrology in Global River Basins in the Off-Line Land-Surface GEOS Assimilation (OLGA) System

Land surface hydrology for the Off-line Land-surface GEOS Analysis (OLGA) system and Goddard Earth Observing System (GEOS-1) Data Assimilation System (DAS) has been examined using a river routing model. The GEOS-1 DAS land-surface parameterization is very simple, using an energy balance prediction of surface temperature and prescribed soil water. OLGA uses near-surface atmospheric data from the GEOS-1 DAS to drive a more comprehensive parameterization of the land-surface physics. The two global systems are evaluated using a global river routing model. The river routing model uses climatologic surface runoff from each system to simulate the river discharge from global river basins, which can be compared to climatologic river discharge. Due to the soil hydrology, the OLGA system shows a general improvement in the simulation of river discharge compared to the GEOS-1 DAS. Snowmelt processes included in OLGA also have a positive effect on the annual cycle of river discharge and source runoff. Preliminary tests of a coupled land-atmosphere model indicate improvements to the hydrologic cycle compared to the uncoupled system. The river routing model has provided a useful tool in the evaluation of the GCM hydrologic cycle, and has helped quantify the influence of the more advanced land surface model

Internal Physical Features of a Land Surface Model Employing a Tangent Linear Model

The Earth's land surface, including its biomass, is an integral part of the Earth's weather and climate system. Land surface heterogeneity, such as the type and amount of vegetative covering., has a profound effect on local weather variability and therefore on regional variations of the global climate. Surface conditions affect local weather and climate through a number of mechanisms. First, they determine the re-distribution of the net radiative energy received at the surface, through the atmosphere, from the sun. A certain fraction of this energy increases the surface ground temperature, another warms the near-surface atmosphere, and the rest evaporates surface water, which in turn creates clouds and causes precipitation. Second, they determine how much rainfall and snowmelt can be stored in the soil and how much instead runs off into waterways. Finally, surface conditions influence the near-surface concentration and distribution of greenhouse gases such as carbon dioxide. The processes through which these mechanisms interact with the atmosphere can be modeled mathematically, to within some degree of uncertainty, on the basis of underlying physical principles. Such a land surface model provides predictive capability for surface variables including ground temperature, surface humidity, and soil moisture and temperature. This information is important for agriculture and industry, as well as for addressing fundamental scientific questions concerning global and local climate change. In this study we apply a methodology known as tangent linear modeling to help us understand more deeply, the behavior of the Mosaic land surface model, a model that has been developed over the past several years at NASA/GSFC. This methodology allows us to examine, directly and quantitatively, the dependence of prediction errors in land surface variables upon different vegetation conditions. The work also highlights the importance of accurate soil moisture information. Although surface variables are predicted imperfectly due to inherent uncertainties in the modeling process, our study suggests how satellite observations can be combined with the model, through land surface data assimilation, to improve their prediction

Intraparenchymal Striatal Transplants Required for Maintenance of Behavioral Recovery in an Animal Model of Huntington's Disease

Rats which receive injections of kainic acid (KA) into the striatum show many of the anatomical, biochemical and behavioral abnormalities seen in patients with Huntington's disease. Recently, it has been reported that fetal striatal transplants into the lesioned striatum could normalize the neurological and behavioral abnormalities produced by the KA lesion. The present study examined the issue of transplant integration in producing behavioral recovery. In one experiment, lesioned animals with transplants located within the lateral ventricle were compared against parenchymally transplanted rats. It was found that unless the ventricular transplant grew into the lesioned striatum there was no recovery. The second experiment demonstrated that electrolytic destruction of a successful fetal striatal transplant could reverse the transplant-induced behavioral recovery. These results suggest that the integrity of the transplant is important in maintaining behavioral recovery. A continuing functional interaction between the host brain and transplanted tissue may be a vital element in the success of the fetal striatal transplant

Assimilation of Satellite-Based Snow Cover and Freeze/Thaw Observations Over High Mountain Asia

Toward qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA) framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted: (1) the assimilation of a satellite-derived snow cover map (MOD10A1) and (2) the assimilation of the NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing-based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 of 14) show slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77 K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0–10 cm soil temperature are reduced (on average) by 10 and 7%, respectively. The improvements in the top layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10–40 cm soil temperature are reduced (on average) by 9 and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes

Investigation of the 2006 Drought and 2007 Flood Extremes at the Southern Great Plains Through an Integrative Analysis of Observations

Hydrological years 2006 (HY06, 10/2005-09/2006) and 2007 (HY07, 10/2006-09/2007) provide a unique opportunity to examine hydrological extremes in the central US because there are no other examples of two such highly contrasting precipitation extremes occurring in consecutive years at the Southern Great Plains (SGP) in recorded history. The HY06 annual precipitation in the state of Oklahoma, as observed by the Oklahoma Mesonet, is around 61% of the normal (92.84 cm, based on the 1921-2008 climatology), which results in HY06 the second-driest year in the record. In particular, the total precipitation during the winter of 2005-06 is only 27% of the normal, and this winter ranks as the driest season. On the other hand, the HY07 annual precipitation amount is 121% of the normal and HY07 ranks as the seventh-wettest year for the entire state and the wettest year for the central region of the state. Summer 2007 is the second-wettest season for the state. Large-scale dynamics play a key role in these extreme events. During the extreme dry period (10/2005-02/2006), a dipole pattern in the 500-hPa GH anomaly existed where an anomalous high was over the southwestern U.S. region and an anomalous low was over the Great Lakes. This pattern is associated with inhibited moisture transport from the Gulf of Mexico and strong sinking motion over the SGP, both contributing to the extreme dryness. The precipitation deficit over the SGP during the extreme dry period is clearly linked to significantly suppressed cyclonic activity over the southwestern U.S., which shows robust relationship with the Western Pacific (WP) teleconnection pattern. The precipitation events during the extreme wet period (May-July 2007) were initially generated by active synoptic weather patterns, linked with moisture transport from the Gulf of Mexico by the northward low level jet, and enhanced by the mesoscale convective systems. Although the drought and pluvial conditions are dominated by large-scale dynamic patterns, we have demonstrated that the two positive feedback processes during the extreme dry and wet periods found in this study play a key role to maintain and reinforce the length and severity of existing drought and flood events. For example, during the extreme dry period, with less clouds, LWP, PWV, precipitation, and thinner Cu cloud thickness, more net radiation was absorbed and used to evaporate water from the ground. The evaporated moisture, however, was removed by low-level divergence. Thus, with less precipitation and removed atmospheric moisture, more absorbed incoming solar radiation was used to increase surface temperature and to make the ground drier

The observed state of the water cycle in the early twenty-first century

Author Posting. © American Meteorological Society, 2015. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 28 (2015): 8289–8318, doi:10.1175/JCLI-D-14-00555.1.This study quantifies mean annual and monthly fluxes of Earth’s water cycle over continents and ocean basins during the first decade of the millennium. To the extent possible, the flux estimates are based on satellite measurements first and data-integrating models second. A careful accounting of uncertainty in the estimates is included. It is applied within a routine that enforces multiple water and energy budget constraints simultaneously in a variational framework in order to produce objectively determined optimized flux estimates. In the majority of cases, the observed annual surface and atmospheric water budgets over the continents and oceans close with much less than 10% residual. Observed residuals and optimized uncertainty estimates are considerably larger for monthly surface and atmospheric water budget closure, often nearing or exceeding 20% in North America, Eurasia, Australia and neighboring islands, and the Arctic and South Atlantic Oceans. The residuals in South America and Africa tend to be smaller, possibly because cold land processes are negligible. Fluxes were poorly observed over the Arctic Ocean, certain seas, Antarctica, and the Australasian and Indonesian islands, leading to reliance on atmospheric analysis estimates. Many of the satellite systems that contributed data have been or will soon be lost or replaced. Models that integrate ground-based and remote observations will be critical for ameliorating gaps and discontinuities in the data records caused by these transitions. Continued development of such models is essential for maximizing the value of the observations. Next-generation observing systems are the best hope for significantly improving global water budget accounting.This research was funded by multiple grants from NASA’s Energy and Water Cycle Study (NEWS) program.2016-05-0

Forecasting the response of Earth's surface to future climatic and land use changes: a review of methods and research needs

In the future, Earth will be warmer, precipitation events will be more extreme, global mean sea level will rise, and many arid and semiarid regions will be drier. Human modifications of landscapes will also occur at an accelerated rate as developed areas increase in size and population density. We now have gridded global forecasts, being continually improved, of the climatic and land use changes (C&LUC) that are likely to occur in the coming decades. However, besides a few exceptions, consensus forecasts do not exist for how these C&LUC will likely impact Earth-surface processes and hazards. In some cases, we have the tools to forecast the geomorphic responses to likely future C&LUC. Fully exploiting these models and utilizing these tools will require close collaboration among Earth-surface scientists and Earth-system modelers. This paper assesses the state-of-the-art tools and data that are being used or could be used to forecast changes in the state of Earth's surface as a result of likely future C&LUC. We also propose strategies for filling key knowledge gaps, emphasizing where additional basic research and/or collaboration across disciplines are necessary. The main body of the paper addresses cross-cutting issues, including the importance of nonlinear/threshold-dominated interactions among topography, vegetation, and sediment transport, as well as the importance of alternate stable states and extreme, rare events for understanding and forecasting Earth-surface response to C&LUC. Five supplements delve into different scales or process zones (global-scale assessments and fluvial, aeolian, glacial/periglacial, and coastal process zones) in detail