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At least 685 records · Page 38
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NASA NTRS2016

On the Susceptibility of Cold Tropical Cirrus to Ice Nuclei Abundance

Numerical simulations of cirrus formation in the tropical tropopause layer (TTL) are used to evaluate the impact heterogeneous ice nuclei (IN) abundance on cold cloud microphysical properties. The model includes homogeneous and heterogeneous ice nucleation, deposition growthsublimation, and sedimentation. Reanalysis temperature and wind fields are used to force the simulations, with addition of high-frequency wave temperature variability based on in situ measurements. The model results are constrained by recent in situ observations of TTL cirrus and relative humidity, as well as satellite measurements of cirrus bulk properties and occurrence frequencies. Temperature variability driven by high-frequency waves has a dominant influence on TTL cirrus microphysical properties and occurrence frequencies, and inclusion of these waves is required to produce agreement between the simulated and observed cloud properties as well as the abundance of TTL cirrus. With a composition-independent supersaturation threshold for homogeneous freezing of aqueous aerosols, the model produces excessive ice concentrations compared with in situ observations. Inclusion of relatively numerous heterogeneous nuclei ( 100L) in the simulations improves the agreement with observed ice concentrations. However, when IN contribute significantly to TTL cirrus ice nucleation, the occurrence frequency of large supersaturations with respect to ice is less than indicated by in situ measurements. The simulated TTL cirrus extinction statistics agree with observations (within uncertainties) from both in situ and remote-sensing measurements. We find that the sensitivity of TTL cirrus extinction and ice water content statistics to heterogeneous ice nuclei abundance is relatively weak. Likewise, the simulated occurrence frequencies of TTL cirrus are insensitive to ice nuclei abundance, both in terms of cloud frequency height distribution and regional distribution throughout the tropics.

occurences↗
NASA NTRS1996

Radiative Effects of Aerosols Generated from Biomass Burning, Dust Storms, and Forest Fires

Atmospheric aerosol particles, both natural and anthropogenic, are important to the earth's radiative balance. They scatter the incoming solar radiation and modify the shortwave reflective properties of clouds by acting as Cloud Condensation Nuclei (CCN). Although it has been recognized that aerosols exert a net cooling influence on climate (Twomey et al. 1984), this effect has received much less attention than the radiative forcings due to clouds and greenhouse gases. The radiative forcing due to aerosols is comparable in magnitude to current anthropogenic greenhouse gas forcing but opposite in sign (Houghton et al. 1990). Atmospheric aerosol particles generated from biomass burning, dust storms and forest fires are important regional climatic variables. A recent study by Penner et al. (1992) proposed that smoke particles from biomass burning may have a significant impact on the global radiation balance. They estimate that about 114 Tg of smoke is produced per year in the tropics through biomass burning. The direct and indirect effects of smoke aerosol due to biomass burning could add up globally to a cooling effect as large as 2 W/sq m. Ackerman and Chung (1992) used model calculations and the Earth Radiation Budget Experiment (ERBE) data to show that in comparison to clear days, the heavy dust loading over the Saudi Arabian peninsula can change the Top of the Atmosphere (TOA) clear sky shortwave and longwave radiant exitance by 40-90 W/sq m and 5-20 W/sq m, respectively. Large particle concentrations produced from these types of events often are found with optical thicknesses greater than one. These aerosol particles are transported across considerable distances from the source (Fraser et al. 1984). and they could perturb the radiative balance significantly. In this study, the regional radiative effects of aerosols produced from biomass burning, dust storms and forest fires are examined using the Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) data and the instantaneous scanner ERBE data from the NOAA-9 and NOAA-10 satellites.

Christopher Sundar A.↗
NASA NTRS1996

Subsonic Aircraft Soot: A Tracer Documenting Stratospheric Vertical Mixing and Barriers to Inter-Hemispheric Exchanges

Pole-to-pole variability of soot aerosol from subsonic aircraft is evidence of two important aspects of stratospheric transport. Vertical transport to 20 km pressure altitude from flight levels near 10-12 km cannot be explained by isentropic mixing. Instead, lofting in the tropics is a possibility. A strong meridional gradient implies that stratospheric soot aerosol residence time is shorter than are mixing times between the hemispheres. Therefore, little if any of exhaust constituents (with residence times similar to that of aircraft soot aerosol), emitted in heavily traveled flight corridors in northern mid-latitudes by a future supersonic fleet, would be transported to the southern hemisphere. However, a significant fraction of NOx could be lofted to altitudes above flight levels where it would dominate ozone depletion.

Pueschel, Rudolf F.↗
NASA NTRS2012

Development of a Climate Record of Tropospheric and Stratospheric Column Ozone from Satellite Remote Sensing: Evidence of an Early Recovery of Global Stratospheric Ozone

Ozone data beginning October 2004 from the Aura Ozone Monitoring Instrument (OMI) and Aura Microwave Limb Sounder (MLS) are used to evaluate the accuracy of the Cloud Slicing technique in effort to develop long data records of tropospheric and stratospheric ozone and for studying their long-term changes. Using this technique, we have produced a 32-yr (1979-2010) long record of tropospheric and stratospheric column ozone from the combined Total Ozone Mapping Spectrometer (TOMS) and OMI. Analyses of these time series suggest that the quasi-biennial oscillation (QBO) is the dominant source of inter-annual variability of stratospheric ozone and is clearest in the Southern Hemisphere during the Aura time record with related inter-annual changes of 30- 40 Dobson Units. Tropospheric ozone for the long record also indicates a QBO signal in the tropics with peak-to-peak changes varying from 2 to 7 DU. The most important result from our study is that global stratospheric ozone indicates signature of a recovery occurring with ozone abundance now approaching the levels of year 1980 and earlier. The negative trends in stratospheric ozone in both hemispheres during the first 15 yr of the record are now positive over the last 15 yr and with nearly equal magnitudes. This turnaround in stratospheric ozone loss is occurring about 20 yr earlier than predicted by many chemistry climate models. This suggests that the Montreal Protocol which was first signed in 1987 as an international agreement to reduce ozone destroying substances is working well and perhaps better than anticipated.

ozone↗
NASA NTRS2019

Hurricane Stalling Along the North American Coast and Implications for Rainfall

The average speed of tropical cyclone (TC) translation has slowed since the mid 20th century. Here we report that North Atlantic (NA) TCs have become increasingly likely to "stall" near the coast, spending many hours in confined regions. The stalling is driven not only by slower translation, but also by an increase in abrupt changes of direction. We compute residence-time distributions for TCs in confined coastal regions, and find that the tails of these distributions have increased significantly. We also show that TCs stalling over a region result in more rain on the region. Together, increased stalling and increased rain during stalls imply increased coastal rainfall from TCs, other factors equal. Although the data are sparse, we do in fact find a significant positive trend in coastal annual-mean rainfall 1948-2017 from TCs that stall, and we verify that this is due to increased stalling frequency. We make no attribution to anthropogenic climate forcing for the stalling or rainfall; the trends could be due to low frequency natural variability. Regardless of the cause, the significant increases in TC stalling frequency and high potential for associated increases in rainfall have very likely exacerbated TC hazards for coastal populations.

tropical cyclone tracks↗
NASA NTRS2021

Planetary Boundary Layer Height Estimates From ICESat-2 and CATS Backscatter Measurements

The lowest layer of the atmosphere in which all human activity occurs is called the Planetary Boundary Layer (PBL). All physical interactions with the surface, such as heat and moisture transport, pollution dispersion and transport happen in this relatively shallow layer. The ability to understand and model the complex interactions that occur in the PBL is very important to air quality, weather prediction and climate modeling. A fundamental and physically important property of the PBL is its thickness or height. This work presents two methods to obtain global PBL height using satellite lidar data from the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and the Cloud-Aerosol Transport System (CATS). The first method is a straightforward backscatter threshold technique and the second is a machine learning approach known as a Convolutional Neural Network. The PBL height retrievals from the two methods are compared with each other and with PBL height from the NASA GEOS MERRA-2 reanalysis. The lidar-retrieved PBL heights have a high degree of spatial correlation with the model heights but are generally higher over ocean (∼400 m) and over northern hemisphere high latitude regions (∼1,000 m). Over mid-latitude and tropical land areas, the satellite estimated PBL heights agree well with model mid-day estimates. This work demonstrates the feasibility of using satellite lidar backscatter measurements to obtain global PBL height estimates, as well as determining seasonal and regional variability of PBL height.

Stephen P Palm↗
NASA NTRS2007

Transport in the Subtropical Lowermost Stratosphere during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment

We use in situ measurements of water vapor (H2O), ozone (O3), carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), and total reactive nitrogen (NOy) obtained during the CRYSTAL-FACE campaign in July 2002 to study summertime transport in the subtropical lowermost stratosphere. We use an objective methodology to distinguish the latitudinal origin of the sampled air masses despite the influence of convection, and we calculate backward trajectories to elucidate their recent geographical history. The methodology consists of exploring the statistical behavior of the data by performing multivariate clustering and agglomerative hierarchical clustering calculations and projecting cluster groups onto principal component space to identify air masses of like composition and hence presumed origin. The statistically derived cluster groups are then examined in physical space using tracer-tracer correlation plots. Interpretation of the principal component analysis suggests that the variability in the data is accounted for primarily by the mean age of air in the stratosphere, followed by the age of the convective influence, and last by the extent of convective influence, potentially related to the latitude of convective injection (Dessler and Sherwood, 2004). We find that high-latitude stratospheric air is the dominant source region during the beginning of the campaign while tropical air is the dominant source region during the rest of the campaign. Influence of convection from both local and nonlocal events is frequently observed. The identification of air mass origin is confirmed with backward trajectories, and the behavior of the trajectories is associated with the North American monsoon circulation.

Pittman, Jasna V.↗
NASA NTRS2012

Interannual Variability of OLR as Observed by AIRS and CERES

This paper compares spatial anomaly time series of OLR (Outgoing Longwave Radiation) and OLR(sub CLR) (Clear Sky OLR) as determined using observations from CERES Terra and AIRS over the time period September 2002 through June 2011. Both AIRS and CERES show a significant decrease in global mean and tropical mean OLR over this time period. We find excellent agreement of the anomaly time-series of the two OLR data sets in almost every detail, down to 1 deg X 1 deg spatial grid point level. The extremely close agreement of OLR anomaly time series derived from observations by two different instruments implies that both sets of results must be highly stable. This agreement also validates to some extent the anomaly time series of the AIRS derived products used in the computation of the AIRS OLR product. The paper also examines the correlations of anomaly time series of AIRS and CERES OLR, on different spatial scales, as well as those of other AIRS derived products, with that of the NOAA Sea Surface Temperature (SST) product averaged over the NOAA Nino-4 spatial region. We refer to these SST anomalies as the El Nino Index. Large spatially coherent positive and negative correlations of OLR anomaly time series with that of the El Nino Index are found in different spatial regions. Anomalies of global mean, and especially tropical mean, OLR are highly positively correlated with the El Nino Index. These correlations explain that the recent global and tropical mean decreases in OLR over the period September 2002 through June 2011, as observed by both AIRS and CERES, are primarily the result of a transition from an El Nino condition at the beginning of the data record to La Nina conditions toward the end of the data period. We show that the close correlation of global mean, and especially tropical mean, OLR anomalies with the El Nino Index can be well accounted for by temporal changes of OLR within two spatial regions which lie outside the NOAA Nino-4 region, in which anomalies of cloud cover and mid-tropospheric water vapor are both highly negatively correlated with the El Nino Index. Agreement of the AIRS and CERES OLR(sub CLR) anomaly time series is less good, which may be a result of the large sampling differences in the ensemble of cases included in each OLR(sub CLR) data set.

Susskind, Joel↗
NASA NTRS2013

A Single-column Model Ensemble Approach Applied to the TWP-ICE Experiment

Single-column models (SCM) are useful test beds for investigating the parameterization schemes of numerical weather prediction and climate models. The usefulness of SCM simulations are limited, however, by the accuracy of the best estimate large-scale observations prescribed. Errors estimating the observations will result in uncertainty in modeled simulations. One method to address the modeled uncertainty is to simulate an ensemble where the ensemble members span observational uncertainty. This study first derives an ensemble of large-scale data for the Tropical Warm Pool International Cloud Experiment (TWP-ICE) based on an estimate of a possible source of error in the best estimate product. These data are then used to carry out simulations with 11 SCM and two cloud-resolving models (CRM). Best estimate simulations are also performed. All models show that moisture-related variables are close to observations and there are limited differences between the best estimate and ensemble mean values. The models, however, show different sensitivities to changes in the forcing particularly when weakly forced. The ensemble simulations highlight important differences in the surface evaporation term of the moisture budget between the SCM and CRM. Differences are also apparent between the models in the ensemble mean vertical structure of cloud variables, while for each model, cloud properties are relatively insensitive to forcing. The ensemble is further used to investigate cloud variables and precipitation and identifies differences between CRM and SCM particularly for relationships involving ice. This study highlights the additional analysis that can be performed using ensemble simulations and hence enables a more complete model investigation compared to using the more traditional single best estimate simulation only.

numerical weather forecasting↗
NASA NTRS1992

Validation of cloud forcing simulated by the National Center for Atmospheric Research Community Climate Model using observations from the Earth Radiation Budget Experiment

Satellite measurements of the effect of clouds on the top of atmosphere radiative energy budget are used to validate model simulations from the National Center for Atmospheric Research Community Climate Model (NCAR CCM). The ability of the NCAR CCM to reproduce the monthly mean global distribution and temporal variability on both daily and seasonal time scales is assessed. The comparison reveals several deficiencies in the CCM cloud representation. Most notable are the difficulties in properly simulating the effect of clouds on the planetary albedo. This problem arises from discrepancies in the model's portrayal of low-level cloudiness and leads to significant errors in the absorbed solar radiation simulated by the model. The CCM performs much better in simulating the effect of clouds on the longwave radiation emitted to space, indicating its relative success in capturing the vertical distribution of cloudiness. The daily variability of the radiative effects of clouds in both the shortwave and longwave spectral regions is systematically overestimated. Analysis of the seasonal variations illustrates a distinct lack of coupling in the seasonal changes in the radiative effects of cloudiness between the tropics and mid-latitudes and between the Northern and Southern Hemisphere. Much of this problem also arises from difficulties in simulating low-level cloudiness, placing further emphasis on the need for better model parameterizations of boundary layer clouds.

Soden, B. J.↗
NASA NTRS1998

The Impact of Very Fine Vertical Resolution on the Climate of the GEOS-2 GCM

Typical General Circulation Models (GCM's) that are used for tropospheric climate studies extend vertically with significant resolution only into the lower stratosphere, while GCM's specifically designed for the study of the stratospheric climate do not finely resolve the tropospheric jet or boundary layer regions. The NASA/Goddard Data Assimilation Office (DAO) Goddard Earth Observing System GCM, (GEOS-2 GCM) which is a component of the GEOS-2 Data Assimilation System (DAS), is configured with a sufficient number of vertical levels to provide very fine resolution in the tropospheric and stratospheric regimes. The present study is aimed at understanding the benefits for accurate simulation of the climate seen in the GEOS-2 GCM due to its choice of vertical resolution. Raising the model lid to the lower mesosphere so as to explicitly resolve the stratospheric circulation, in particular the descent over the winter pole, is included in GEOS-2 to address the common GCM deficiency of a stratospheric cold pole bias. Increasing the number of levels is directed at improving the representation of the planetary boundary layer, the troposphere at the altitude of the sub-tropical jet stream, and the stratosphere. Comparisons of the climate of simulations with three different vertical resolution formulations of the GEOS-2 GCM are presented. In addition to the reduction in the climate bias, the impact of the resolution on model variability will be briefly discussed.

Molod, Andrea↗
NASA NTRS2013

Reduction of Non-uniform Beam Filling Effects by Vertical Decorrelation: Theory and Simulations

Algorithms for estimating precipitation rates from spaceborne radar observations of apparent radar reflectivity depend on attenuation correction procedures. The algorithm suite for the Ku-band precipitation radar aboard the Tropical Rainfall Measuring Mission satellite is one such example. The well-known problem of nonuniform beam filling is a source of error in the estimates, especially in regions where intense deep convection occurs. The error is caused by unresolved horizontal variability in precipitation characteristics such as specific attenuation, rain rate, and effective reflectivity factor. This paper proposes the use of vertical decorrelation for correcting the nonuniform beam filling error developed under the assumption of a perfect vertical correlation. Empirical tests conducted using ground-based radar observations in the current simulation study show that decorrelation effects are evident in tilted convective cells. However, the problem of obtaining reasonable estimates of a governing parameter from the satellite data remains unresolved.

radar reflectivity↗
NASA NTRS2018

Remote Sensing of CDOM, CDOM Spectral Slope, and Dissolved Organic Carbon in the Global Ocean

A Global Ocean Carbon Algorithm Database (GOCAD) has been developed from over 500 oceanographic field campaigns conducted worldwide over the past 30 years including in situ reflectances and coincident satellite imagery, multi- and hyperspectral Chromophoric Dissolved Organic Matter (CDOM) absorption coefficients from 245–715 nm, CDOM spectral slopes in eight visible and ultraviolet wavebands, dissolved and particulate organic carbon (DOC and POC, respectively), and inherent optical, physical, and biogeochemical properties. From field optical and radiometric data and satellite measurements, several semi-analytical, empirical, and machine learning algorithms for retrieving global DOC, CDOM, and CDOM slope were developed, optimized for global retrieval, and validated. Global climatologies of satellite-retrieved CDOM absorption coefficient and spectral slope based on the most robust of these algorithms lag seasonal patterns of phytoplankton biomass belying Case 1 assumptions, and track terrestrial runoff on ocean basin scales. Variability in satellite retrievals of CDOM absorption and spectral slope anomalies are tightly coupled to changes in atmospheric and oceanographic conditions associated with El Niño Southern Oscillation (ENSO), strongly covary with the multivariate ENSO index in a large region of the tropical Pacific, and provide insights into the potential evolution and feedbacks related to sea surface dissolved carbon in a warming climate. Further validation of the DOC algorithm developed here is warranted to better characterize its limitations, particularly in mid-ocean gyres and the southern oceans.

Dissolved organic carbon↗
NASA NTRS2015

Evaluation of the 7-km GEOS-5 Nature Run

This report documents an evaluation by the Global Modeling and Assimilation Office (GMAO) of a two-year 7-km-resolution non-hydrostatic global mesoscale simulation produced with the Goddard Earth Observing System (GEOS-5) atmospheric general circulation model. The simulation was produced as a Nature Run for conducting observing system simulation experiments (OSSEs). Generation of the GEOS-5 Nature Run (G5NR) was motivated in part by the desire of the OSSE community for an improved high-resolution sequel to an existing Nature Run produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), which has served the community for several years. The intended use of the G5NR in this context is for generating simulated observations to test proposed observing system designs regarding new instruments and their deployments. Because NASA's interest in OSSEs extends beyond traditional weather forecasting applications, the G5NR includes, in addition to standard meteorological components, a suite of aerosol types and several trace gas concentrations, with emissions downscaled to 10 km using ancillary information such as power plant location, population density and night-light information. The evaluation exercise described here involved more than twenty-five GMAO scientists investigating various aspects of the G5NR performance, including time mean temperature and wind fields, energy spectra, precipitation and the hydrological cycle, the representation of waves, tropical cyclones and midlatitude storms, land and ocean surface characteristics, the representation and forcing effects of clouds and radiation, dynamics of the stratosphere and mesosphere, and the representation of aerosols and trace gases. Comparisons are made with observational data sets when possible, as well as with reanalyses and other long model simulations. The evaluation is broad in scope, as it is meant to assess the overall realism of basic aspects of the G5NR deemed relevant to the conduct of OSSEs. However, because of the relatively short record and other practical considerations, these comparisons cannot provide a definitive, statistically sound assessment of all model deficiencies, or guarantee the G5NR's suitability for all OSSE applications. Differences between the observed and simulated behavior also must be judged in the context of basic internal atmospheric variability which can introduce variations that are not necessarily controlled by the prescribed sea surface temperatures used in generating the G5NR. The results show that the G5NR performs well as measured by the majority of metrics applied in this evaluation. Particular benefits derived from the 7-km resolution of G5NR include realistic representations of extreme weather events in both the tropics and extratropics including tropical cyclones, Nor'easters and mesoscale convective complexes; improved representation of the diurnal cycle of precipitation over land; well-resolved surface-atmosphere interactions such as katabatic wind flows over Antarctica and Greenland; and resolution of orographically generated gravity waves that propagate into the upper atmosphere and influence the large scale circulation. Obvious deficiencies in the G5NR include a "splitting" of the inter-tropical convergence zone, which leads to a weaker-than-observed Hadley circulation and related deficiencies in the depiction of stationary wave patterns. Also, while the G5NR captures global cloud features and radiative effects well in general, close comparison with observations reveals higher-than-observed cloud brightness, likely due to an overabundance of cloud condensate; less distinct cloud minima in subtropical subsidence zones, consistent with a weak Hadley circualtion; and too few near-coastal marine stratocumulus clouds.

GEOS-5↗
NASA NTRSDate not supplied

Detecting Hail from Space: Algorithms, Climatologies, and Challenges Going Forward

In addition to the myriad threats that severe hailstorms pose to society, infrastructure and agriculture, severe hail is difficult to measure in situ, and surface-based hail reporting and detection methods are inconsistent and subject to geographical or societal biases. This motivates the use of spaceborne remote-sensing platforms to retrieve hail and construct climatologies in a globally uniform way. We have developed a hail detection algorithm that leverages the sensitivity of spaceborne passive-microwave radiometers to scattering by hail, particularly in the channels from 10 to 89 GHz. We use this retrieval to construct global climatologies of severe hail using several different spaceborne sensors: the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Global Precipitation Measurement Mission (GPM) Microwave Imager, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors and are working to extend into the late 1980’s using the Special Sensor Microwave Imager/Sounder (SSMI/(S)) data. Using coincident Global Precipitation Measurement (GPM) Ku-band precipitation radar, we assessed this retrieval and several others in the literature for their effectiveness and regional variability. We developed a passive-microwave algorithm that corresponds tightly to radar reflectivity and gives the least appearance of regional biases compared to other passive-microwave approaches in the literature. Satellite platforms offer consistent observations, even in remote, data-sparse, and oceanic regions that ground-based networks exclude. There are, however, potential disconnects between the processes identified aloft by the satellite and the resultant weather at the ground, leading to uncertainties in the retrievals that may propagate into satellite-based climatologies, particularly in the Tropics, where there are abundant strong - but not necessarily hailing - storms that are strongly represented in the current satellite climatologies. We will discuss ongoing efforts to assess and mitigate the contributing factors to these uncertainties, chiefly among them the effects of non-uniform beam filling in the passive-microwave footprint, and the relationships between the size distributions of hailstones aloft and the dynamic processes and environments with which they interact throughout their trajectories.

Sarah D Bang↗
NASA NTRS2002

Causes of Long-Term Drought in the United States Great Plains

The United States Great Plains (USGP) experienced a number of multi-year droughts during the last century, most notably the droughts of the 1930s and 1950s. This study examines the causes of such droughts using ensembles of long term (1930-1999) simulations carried out with the NASA Seasonal-to-Interannual Prediction Project (NSIPP-1) atmospheric general circulation model (AGCM) forced with observed sea surface temperatures (SSTs). The results show that the model produces long-term (multi-year) variations in the USGP precipitation that are similar to those observed. A correlative analysis suggests that the ensemble mean low frequency (time scales longer than about 6 years) rainfall variations in the USGP are linked to a pan-Pacific pattern of SST variability that is the leading empirical orthogonal function (EOF) in the low frequency SST data. The link between the SST and the Great Plains precipitation is confirmed in idealized AGCM simulations, in which the model is forced by the 2 polarities of the pan-Pacific SST pattern. The idealized simulations further show that it is primarily the tropical part of the SST anomalies that influence the USGP. As such, the USGP tend to have above normal precipitation when the tropical Pacific SSTs are above normal, while there is a tendency for drought when the tropical SSTs are cold. The upper tropospheric response to the pan-Pacific SST EOF shows a global-scale pattern with a strong wave response in the Pacific and a substantial zonally-symmetric component in which USGP pluvial (drought) conditions are associated with reduced (enhanced) heights throughout the extra-tropics. The potential predictability of rainfall in the USGP associated with SSTs is rather modest, with on average about 1/3 of the total low frequency rainfall variance forced by SST anomalies. Further idealized experiments with climatological SST, suggest that the remaining low frequency variance in the USGP precipitation is the result of interactions with soil moisture. In particular, simulations with soil moisture feedback show a six-fold increase in the variance in annual USGP precipitation compared with simulations in which the soil feedback is excluded. In addition to increasing variance, the interactions with the soil introduce year-to-year memory in the hydrological cycle that is consistent with a red noise process, in which the low frequencies in the deep soil are the result of integrating a net forcing (precipitation-evaporation-runoff) that is white noise on interannual time scales. As such, the role of low frequency SST variability is to introduce a bias to the net forcing on the soil moisture that drives the random process preferentially to either wet or dry conditions.

Schubert, Siegfried D.↗
NASA NTRS1997

Satellite Sensed Skin Sea Surface Temperature

Quantitative predictions of spatial and temporal changes the global climate rely heavily on the use of computer models. Unfortunately, such models cannot provide the basis for climate prediction because key physical processes are inadequately treated. Consequently, fine tuning procedures are often used to optimize the fit between model output and observational data and the validation of climate models using observations is essential if model based predictions of climate change are to be treated with any degree of confidence. Satellite Sea Surface Temperature (SST) observations provide high spatial and temporal resolution data which is extremely well suited to the initialization, definition of boundary conditions and, validation of climate models. In the case of coupled ocean-atmosphere models, the SST (or more correctly the 'Skin' SST (SSST)) is a fundamental diagnostic variable to consider in the validation process. Daily global SST maps derived from satellite sensors also provide adequate data for the detection of global patterns of change which, unlike any other SST data set, repeatedly extend into the southern hemisphere extra-tropical regions. Such data are essential to the success of the spatial 'fingerprint' technique, which seeks to establish a north-south asymmetry where warming is suppressed in the high latitude Southern Ocean. Some estimates suggest that there is a greater than 80% chance of directly detecting significant change (97.5 % confidence level) after 10-12 years of consistent global observations of mean sea surface temperature. However, these latter statements should be qualified with the assumption that a negligible drift in the observing system exists and that biases between individual instruments required to derive a long term data set are small. Given that current estimates for the magnitude of global warming of 0.015 K yr(sup -1) - 0.025 K yr(sup -1), satellite SST data sets need to be both accurate and stable if such a warming trend is to be confidently detected. Some of these activities are focussed to develop and deploy instrumentation suitable for the collection of precise in situ measurements of the SSST which can be used to improve the accuracy of satellite measurements, while others develop techniques to generate improved global analyses of sea surface temperature using historical data.

Donlon, Craig↗
NASA NTRS2007

Analysis of The Surface Radiative Budget Using ATLAS Data for San Juan, Puerto Rico

The additional beating 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 man-made 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. Urban landscapes are a complex mixture of vegetated and nonvegetated surfaces. It is difficult to take enough temperature measurements over a large city area to characterize the complexity of urban radiant surface temperature variability. The NASA Airborne Thermal and Land Applications Sensor (ATLAS) operates in the visual and IR bands was used in February 2004 to collect data from San Juan, Puerto Rico with the main objective of investigating the Urban Heat Island (UHI) in tropical cities.

Luvall, Jeffrey C.↗