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NASA NTRS2016

LS3MIP (v1.0) Contribution to CMIP6: The Land Surface, Snow and Soil Moisture Model Intercomparison Project Aims, Setup and Expected Outcome.

The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow, and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth System Models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems).The experiments are subdivided in two components, the first addressing systematic land biases in offline mode (LMIP, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (LFMIP, building upon the GLACE-CMIP blueprint).

atmospheric temperature↗

NASA NTRSDate not supplied

An Update on the Hydrological Land Surface Data and Services at NASA GES DISC

"The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of twelve NASA Earth Observing System data centers that document, process, archive and distribute data from Earth science missions and related projects. The GES DISC hosts many hydrological land surface data products including North American Land Data Assimilation System (NLDAS), Global Land Data Assimilation System (GLDAS), the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), and assimilated-Gravity Recovery and Climate Experiment (GRACE). The Land Surface Model (LSM) products contain model output, including heat fluxes, rain, snow, soil temperature, soil moisture and runoff; and observational forcing data, including surface pressure, temperature, precipitation, downward shortwave and longwave radiation, humidity, and wind. The temporal resolution of the hydrology data at the GES DISC ranges from hourly to monthly and the spatial resolution 0.1 degree to 1.0 degree. The variability of Earth’s water and energy cycles can be characterized with these spatially and temporally consistent, and quality controlled hydrological land surface data. These data could support ongoing modeling activities, and help improve our understanding of land-surface-atmosphere interactions and their impact on climate. Over the years, new data is added, and older data is reprocessed and updated. This presentation provides a summary table of the hydrological data and discusses recent updates to the data and data services at the GES DISC. New data products include (1) GRACE Global Version 3.0 drought indicators and (2) FLDAS monthly global and Central Asia daily data. Reprocessed and updated products include (1) NLDAS Version 2.0 and (2) GRACE United States Version 4.0 drought indicators. Lastly, this presentation will discuss the opportunities for accessing some of our hydrology data from the cloud. As the hydrology land surface data is moved to the cloud, users can expect faster service for longer time series, notably the hydrology data rods time series service."

Ashley Heath↗

NASA NTRS1985

The use of satellite data in understanding and predicting convective and large-scale dynamical processes

A two-layer truncated baroclinic spectral model was developed to study the long-term evolution of disturbances to a baroclinically unstable mean flow. Topography and crudely-parameterized radiative processes were accounted for. As a result of Robert Schlaak's discovery of the underlying barotropic nature of the index oscillation as well as reviewers suggestions about the original manuscript, the model has been revised to allow for barotropic as well as baroclinic wave-mean flow interactions. The form-drag exerted by the topography on the barotropic part of the mean flow is larger than on the baroclinic part and thus researchers anticipate significant changes from the original calculations on the index oscillation when it is strongly modulated by topography. Researchers believe that since the index oscillation accounts for a significant portion of atmospheric temporal variance, the long term predictability could be improved if reliable forecasts of the index oscillation were available. Two spectral models of the index oscillation, one barotropic and the other baroclinic, have been developed. The latter allows for moisture, radiation, land-sea temperature countrasts, and energy exchanges with the underlying surface.

Dutton, J. A.↗

NASA NTRS1999

Earth Observing System: Present Capabilities and Promises for the Future

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. During this year 5 EOS science missions are scheduled for launch, representing observations of (i) total solar irradiance, (ii) Earth radiation budget, (iii) land cover & land use change, (iv) ocean processes (vector wind, sea surface temperature, and ocean color), (v) atmospheric processes (aerosol and cloud properties, water vapor, and temperature and moisture profiles), and (vi) atmospheric chemistry (both tropospheric and stratospheric). In succeeding years many more satellites will be launched that will contribute immeasurably to our understanding of the Earth's environment. In this presentation I will describe how scientists intend to use MODIS, an earth-viewing cross-track scanning spectroradiometer to be launched on the Terra satellite in summer 1999, for the remote sensing of cloud and aerosol properties. MODIS will scan a swath width sufficient to provide nearly complete global coverage every two days from a polar-orbiting, sun-synchronous, platform at an altitude of 705 km. In addition, I will describe key elements of other instruments recently launched or planned for flight in the coming months. Finally, I will lay out a plan for the future space-based observing system being planned by NASA and its partners.

King, Michael D.↗

NASA NTRS2007

The Global Precipitation Climatology Project (GPCP): Results, Status and Future

The Global Precipitation Climatology Project (GPCP) is one of a number of long-term, satellite-based, global analyses routinely produced under the auspices of the World Climate Research Program (WCRP) and its Global Energy and Watercycle EXperiment (GEWEX) program. The research quality analyses are produced a few months after real-time through the efforts of scientists at various national agencies and universities in the U.S., Europe and Japan. The primary product is a monthly analysis of surface precipitation that is globally complete and spans the period 1979-present. There are also pentad analyses for the same period and a daily analysis for the 1997-present period. Although generated with somewhat different data sets and analysis schemes, the pentad and daily data sets are forced to agree with the primary monthly analysis on a grid box by grid box basis. The primary input data sets are from low-orbit passive microwave observations, geostationary infrared observations and surface raingauge information. Examples of research with the data sets are discussed, focusing on tropical (25N-25s) rainfall variations and possible long-term changes in the 28-year (1979-2006) monthly dataset. Techniques are used to discriminate among the variations due to ENSO, volcanic events and possible long-term changes for rainfall over both land and ocean. The impact of the two major volcanic eruptions over the past 25 years is estimated to be about a 5% maximum reduction in tropical rainfall during each event. Although the global change of precipitation in the data set is near zero, a small upward linear change over tropical ocean (0.06 mm/day/l0yr) and a slight downward linear change over tropical land (-0.03 mm/day/l0yr) are examined to understand the impact of the inhomogeneity in the data record and the length of the data set. These positive changes correspond to about a 5% increase (ocean) and 3% increase (ocean plus land) during this time period. Relations between variations in surface temperature and precipitation are analyzed on seasonal to inter-decadal time scales. A new, version 3 of GPCP is being planned to incorporate new satellite information (e.g., TRMM) and provide higher spatial and temporal resolution for at least part of the data record. The goals and plans for that GPCP re-processing will be outlined.

Adler, Robert F.↗

NASA NTRS2008

Transient Thermal Model and Analysis of the Lunar Surface and Regolith for Cryogenic Fluid Storage

A transient thermal model of the lunar surface and regolith was developed along with analytical techniques which will be used to evaluate the storage of cryogenic fluids at equatorial and polar landing sites. The model can provide lunar surface and subsurface temperatures as a function of latitude and time throughout the lunar cycle and season. It also accounts for the presence of or lack of the undisturbed fluff layer on the lunar surface. The model was validated with Apollo 15 and Clementine data and shows good agreement with other analytical models.

Christie, Robert J.↗

NASA NTRS2010

The Response of African Land Surface Phenology to Large Scale Climate Oscillations

Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the African continent. Analysis of changes in phenology can provide quantitative information on the effect of climate variability on growing seasons in agricultural regions. Using a robust statistical methodology, we describe the relationship between phenology metrics derived from the 26 year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), and the Multivariate ENSO Index (MEI). We map the most significant positive and negative correlation for the four climate indices in Eastern, Western and Southern Africa between two phenological metrics and the climate indices. Our objective is to provide evidence of whether climate variability captured in the four indices has had a significant impact on the vegetative productivity of Africa during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by large scale variations in climate. The particular climate index and the timing showing highest correlation depended heavily on the region examined. In Western Africa the cumulative NDVI correlates with PDO in September-November. In Eastern Africa the start of the June-October season strongly correlates with PDO in March-May, while the PDO in December-February correlates with the start of the February-June season. The cumulative NDVI over this last season relates to the MEI of March-May. For Southern Africa, high correlations exist between SOS and NAO of September-November, and cumulative NDVI and MEI of March-May. The research shows that climate indices can be used to anticipate late start and variable vigor in the growing season of sensitive agricultural regions in Africa.

Brown, Molly E.↗

NASA NTRS2013

Characteristics of Mesoscale Organization in WRF Simulations of Convection during TWP-ICE

Compared to satellite-derived heating profiles, the Goddard Institute for Space Studies general circulation model (GCM) convective heating is too deep and its stratiform upper-level heating is too weak. This deficiency highlights the need for GCMs to parameterize the mesoscale organization of convection. Cloud-resolving model simulations of convection near Darwin, Australia, in weak wind shear environments of different humidities are used to characterize mesoscale organization processes and to provide parameterization guidance. Downdraft cold pools appear to stimulate further deep convection both through their effect on eddy size and vertical velocity. Anomalously humid air surrounds updrafts, reducing the efficacy of entrainment. Recovery of cold pool properties to ambient conditions over 5-6 h proceeds differently over land and ocean. Over ocean increased surface fluxes restore the cold pool to prestorm conditions. Over land surface fluxes are suppressed in the cold pool region; temperature decreases and humidity increases, and both then remain nearly constant, while the undisturbed environment cools diurnally. The upper-troposphere stratiform rain region area lags convection by 5-6 h under humid active monsoon conditions but by only 1-2 h during drier break periods, suggesting that mesoscale organization is more readily sustained in a humid environment. Stratiform region hydrometeor mixing ratio lags convection by 0-2 h, suggesting that it is strongly influenced by detrainment from convective updrafts. Small stratiform region temperature anomalies suggest that a mesoscale updraft parameterization initialized with properties of buoyant detrained air and evolving to a balance between diabatic heating and adiabatic cooling might be a plausible approach for GCMs.

Del Genio, Anthony D.↗

NASA NTRS2018

Satellite and Reanalysis Air Quality Data and Services at NASA GES DISC for Public Health Study

Outbreaks of infectious diseases and health can be influenced by airborne and water-borne pollutants. Furthermore, air and water quality are associated with climate variability, industrialization, land use and land cover change, and water resource management. It is therefore crucial to understand environment-disease connections with existing long-term observed and modeled data, particularly for development of early warning systems for infectious disease outbreaks. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) (https://disc.gsfc.nasa.gov) archives large volumes of global environment data that are useful for research and applications regarding environmental factors and public health. Examples of air quality measurements are: Daily satellite remotely sensed data, including Aerosol Index (AI), O3, SO2, CO, and NO2 from Aura/OMI (October 2014 to present), and OMPS-NPP (January 2012 to present, currently research data products only) Hourly and monthly reanalysis modeled data, including PM2.5, O3, CO, SO2, BC, dust, AOD, and aerosol types from MERRA-2 (January 1980 to present) Examples of surface meteorology and land surface measurements are: hourly, daily, and monthly satellite precipitation from TRMM (December 1997 to March 2015) 30-minute and monthly satellite precipitation from GPM (March 2014 to present); Hourly and monthly modeled surface meteorology and land surface condition from MERRA-2 (January 1980 to present) and land surface assimilation models (January 1948 to present), including precipitation, surface temperature, relative humidity, wind, and soil moisture.This presentation will give an overview of relevant environmental data at the NASA GES DISC. Through a number of use cases, such as dust events and active fires, we will introduce data services that assist in finding the right data, enable visualization and analysis of the data online, and allow downloading of data in user-preferred format.

remote sensing data↗

NASA NTRS1998

Seasonal-to-Interannual Precipitation Variability and Predictability in a Coupled Land-Atmosphere System

In an earlier GCM study, we showed that interactive land surface processes generally contribute more to continental precipitation variance than do variable sea surface temperatures (SSTs). A new study extends this result through an analysis of 16-member ensembles of multi-decade GCM simulations. We can now show that in many regions, although land processes determine the amplitude of the interannual precipitation anomalies, variable SSTs nevertheless control their timing. The GCM data can be processed into indices that describe geographical variations in (1) the potential for seasonal-to-interannual prediction, and (2) the extent to which the predictability relies on the proper representation of land-atmosphere feedback.

Koster, Randal D.↗

NASA NTRS1980

Remote estimation of the surface characteristics and energy balance over an urban-rural area and the effects of surface heat flux on plume spread and concentration

A one dimensional boundary layer model was used in conjunction with satellite derived infrared surface temperatures to deduce values of moisture availability, thermal inertia, heat and evaporative fluxes. The Penn State satellite image display system, a sophisticated image display facility, was used to remotely sense these various parameters for three cases: St. Louis, Missouri; the Land Between the Lakes, Kentucky; and Clarksville, Tennessee. The urban centers displayed the maximum daytime surface temperatures which correspond to the minimum values of moisture availability. The urban center of St. Louis and the bodies of water displayed the maximum nighttime surface temperatures which correspond to the maximum thermal inertia values. It is shown that moisture availability and thermal inertia are very much responsible for the formation of important temperature variations over the urban rural complex.

Dicristofaro, D. C.↗

NASA NTRS2019

Long-Lived In-Situ Solar System Explorer (LLISSE): Potential Contributions to the Next Decade of Solar System Exploration

Venus, while having similar size, mass, and location in the solar system to Earth, varies from Earth in many ways and holds many scientific mysteries despite many missions that have focused on it in the past. Primary differences include Venus' climate, atmosphere, and perhaps most notably the extreme surface conditions. The layers of sulfuric acid clouds and high pressure CO2 laden atmosphere make remote sensing at Venus much less effective than at other solar system bodies. In addition, surface conditions present formidable engineering challenges due to the high temperature, pressure, and reactive chemistry. To date, landed missions have not been able to last more than about 2 hours on the surface [1]. This has resulted in significant knowledge gaps about the surface conditions of this important body in the solar system. The science community has effectively no in-situ temporal data at the Venus surface. These data are critical for the development of a thorough understanding of Venus' weather and the processes by which chemical species interact with each other and are transported throughout the atmospheric column. The LLISSE platform, and its variants, are a foundation for future mission concepts based on a core set of long-lived technologies providing significant new science as well as demonstrating new technical capabilities. After completion, LLISSE has the potential to be a complimentary element to missions going to Venus and would provide unique and important science to missions whether they be orbiters or short duration landers.

Kremic, Tibor↗

NASA NTRS2019

Long-Lived In-Situ Solar System Explorer (LLISSE)

Kremic, Tibor↗

NASA NTRS2009

Radiative Forcing and Temperature Response to Changes in Urban Albedos and Associated CO2 Offsets

The two main forcings that can counteract to some extent the positive forcings from greenhouse gases from pre-industrial times to present-day are the aerosol and related aerosol-cloud forcings, and the radiative response to changes in surface albedo. Here, we quantify the change in radiative forcing and surface temperature that may be obtained by increasing the albedos of roofs and pavements in urban areas in temperate and tropical regions of the globe. Using the catchment land surface model (the land model coupled to the GEOS-5 Atmospheric General Circulation Model), we quantify the response of the total outgoing (outgoing shortwave+longwave) radiation to urban albedo changes. Globally, the total outgoing radiation increased by 0.5 W/square m and temperature decreased by -0.008 K for an average 0.003 increase in albedo. For the U.S. the total outgoing total radiation increased by 2.3 W/square meter, and temperature decreased by approximately 0.03 K for an average 0.01 increase in albedo. These values are for the boreal summer (Tune-July-August). Based on these forcings, the expected emitted CO2 offset for a plausible 0.25 and 0.15 increase in albedos of roofs and pavements, respectively, for all global urban areas, was found to be approximately 57 Gt CO2 . A more meaningful evaluation of the impacts of urban albedo increases on climate and the expected CO2 offsets would require simulations which better characterizes urban surfaces and represents the full annual cycle.

Menon, Surabi↗

NASA NTRS2011

The Use of ATLAS Data to Quantify Surface Radiative Budgets in Four US Cities

The additional heating of the air over the city is the result of the replacement of naturally vegetated surfaces with those composed of asphalt, concrete, rooftops and other manmade materials. The temperatures of these artificial surfaces can be 20 to 40 C higher than vegetated surfaces. This produces a dome of elevated air temperatures 5 to 8 C greater over the city, compared to the air temperatures over adjacent rural areas. This effect is called the "urban heat island". Urban landscapes are a complex mixture of vegetated and non-vegetated surfaces. It is difficult to take enough temperature measurements over a large city area to. The use of remotely sensed data from airborne scanners is ideal to characterize the complexity of urban albedo and radiant surface temperatures. The National Aeronautics and Space Administration (NASA) Airborne Thermal and Land Applications Sensor (ATLAS) operates in the visual and IR bands was used to collect data from Salt Lake City, UT, Sacramento, CA, Baton Rouge, LA. And San Juan, Puerto Rico with the main objective of investigating the Urban Heat Island (UHI). In this presentation we will examine the techniques of analyzing remotely sensed data for measuring the effect of various urban surfaces on their contribution to the urban heat island effect.

Luvall, Jeffrey↗

NASA NTRS2017

Understanding Arctic Surface Temperature Differences in Reanalyses

Reanalyses in the Arctic are widely used for model evaluation and for understanding contemporary climate change. Nevertheless, differences among reanalyses in fundamental meteorological variables including surface air temperature are large. For example, the 1980-2009 mean surface air temperature for the north polar cap (70ÂdegN-90ÂdegN) among global reanalyses span a range of 2.4 K, which approximates the average warming trend from these reanalyses over the 30-year period of 2.1 K. Understanding these differences requires evaluation over the three principal surface domains of the Arctic: glaciated land, the unglaciated terrestrial surface, and sea ice/ocean. An examination is conducted of contemporary global reanalyses of the ECMWF Interim project, NASA MERRA, MERRA-2, JRA-55, and NOAA CFSR using available in situ data and assessments of the surface energy budget. Overly-simplistic representations of the Greenland Ice Sheet surface are found to be associated with local warm air temperature biases in winter. A review of progress made in the development of the MERRA-2 land-ice representation is presented. Large uncertainty is also found in temperatures over the Arctic tundra and boreal forest zone. But a key focus of temperature differences for northern high latitudes is the Arctic Ocean. Near-surface air temperature differences over the Arctic Ocean are found to be related to discrepancies in sea ice and sea surface temperature boundary data, which are severely compromised in current reanalyses. Issues with the modeled representation of sea ice cover are an additional factor in reanalysis temperature trends. Differences in the representation of the surface energy budget among the various reanalyses are also reviewed.

Surface Temperatur↗

NASA NTRS2022

Uncertainty Estimation for SMAP Level-1 Brightness Temperature Assimilation at Different Timescales

Soil Moisture Active Passive (SMAP) mission brightness temperature (T(b) ) observations are assimilated into NASA’s Catchment Land Surface Model using an Ensemble Kalman filter to update simulations of surface and root-zone soil moisture. Different time series components of the T(b) observations are assimilated including anomalies, inter-annual variations, and high frequency variations. To optimize the weights that the data assimilation (DA) puts on the observations, the ratio between the uncertainties of modeled and observed T(b) is approximated using modeled and observed soil moisture uncertainties estimated using triple collocation analysis. In a benchmark experiment, T(b) observations are assimilated using a spatially constant 4 Kelvin (K) observation uncertainty, as in the operational SMAP Level-4 algorithm. All DA experiments exhibit notable skill improvements in most regions. Improvements are largest for the inter-annual variations in the simulations of both surface and root-zone soil moisture (mean improvements in terms of Pearson correlation (-) are 0.08 and 0.06, respectively). Anomaly simulations improve similarly (0.07), and improvements in the high-frequency variations are only observed for surface soil moisture simulations (0.06). No notable difference in skill - neither improvement nor deterioration - is observed between the experiments that use optimized observation uncertainty parameters and the 4 K benchmark experiment. This may be explained by the presence of large observation operator errors, which are analytically shown to have the potential to render post-update uncertainty insensitive to inaccuracies in estimates of the Kalman gain. These results have important implications for the design of soil moisture DA systems, in particular for parameterizing model and observation uncertainties.

Hydrology↗

NASA NTRS2023

Seasonal Soil Freeze/Thaw Variability Across North America via Ensemble Land Surface Modeling

Land surface modeling provides the opportunity to investigate winter soil processes such as soil freezing and thawing over large domains. However, the variability in simulated winter soil characteristics among land surface models and forcing datasets is not well understood. In this study, a nine-member ensemble was employed to characterize the spatial and inter-annual variability of three winter soil characteristics, annual number of frozen days, annual minimum temperature and annual number of freeze-thaw cycles over North America during the water years 2010–2016. The ensemble, including three land surface model (JULES, Noah2.7.1 and Noah-MP) and three forcing datasets (ECMWF, GDAS and MERRA2), was developed through the Snow Ensemble Uncertainty Project (SEUP). In many regions, there was remarkably good agreement across the ensemble for the winter soil temperatures. However, the variability among the ensemble's annual number of frozen days, as quantified with standard deviation, exceeded 150 days at the northern Pacific coastline. While the differences among the ensemble members' annual minimum temperature were typically <3 °C, the differences exceeded 6 °C north of 50°N. The ensemble members generally agreed within one to three freeze-thaw cycles in mid latitude regions, Alaska, and the south and west coasts of the USA. High variability among the ensemble, more than six freeze-thaw cycles, occurred in the Great Plains, northern Pacific coastline, and along the Appalachian Mountains. Differences in winter soil temperature characteristics were more apparent among the LSMs rather than the meteorological forcing datasets. Except for maritime regions, the Noah2.7.1 members had the fewest annual number of frozen days and the least number of freeze-thaw cycles. Noah2.7.1 also had the coldest annual minimum temperatures except for ephemeral regions. Comparisons between in-situ observations and the SEUP estimates of winter soil characteristics revealed that the modeled frozen period was much longer than observed, that the modeled annual minimum temperatures were much colder than observed, and the modeled freeze-thaw cycles occurred more frequently than observed. Excluding the high latitude sites, the observed frozen period is less than two months with the minimum temperature above − 5 °C at most of the studied sites, while the ensemble members simulated, on average, a four month frozen period with − 10 °C minimum temperature. Errors in capturing snow during the accumulation period appear to impact differences in modeled versus observed soil temperature throughout the entire winter.

Soil freeze/thaw↗