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NASA NTRS1991

Air-sea fluxes and surface layer turbulence around a sea surface temperature front

The observed effects of sharp changes in sea surface temperature (SST) on the air-sea fluxes, surface roughness, and the turbulence structure in the surface layer and the marine atmospheric boundary layer are discussed. In situ flux and turbulence observations were carried out from three aircraft and two ships within the FASINEX framework. Three other aircraft used remote sensors to measure waves, microwave backscatter, and lidar signatures of cloud tops. Descriptions of the techniques, intercomparison of aircraft and ship flux data, and use of different methods for analyzing the fluxes from the aircraft data are described. Changing synoptic weather on three successive days yielded cases of wind direction both approximately parallel and perpendicular to a surface temperature front. For the wind perpendicular to the front, wind over both cold-to-warm and warm-to-cold surface temperatures occurred. Model results consistent with the observations suggest that an internal boundary layer forms at the SST.

Friehe, C. A.↗
NASA NTRS2011

Analysis of Ultra High Resolution Sea Surface Temperature Level 4 Datasets

Sea surface temperature (SST) studies are often focused on improving accuracy, or understanding and quantifying uncertainties in the measurement, as SST is a leading indicator of climate change and represents the longest time series of any ocean variable observed from space. Over the past several decades SST has been studied with the use of satellite data. This allows a larger area to be studied with much more frequent measurements being taken than direct measurements collected aboard ship or buoys. The Group for High Resolution Sea Surface Temperature (GHRSST) is an international project that distributes satellite derived sea surface temperatures (SST) data from multiple platforms and sensors. The goal of the project is to distribute these SSTs for operational uses such as ocean model assimilation and decision support applications, as well as support fundamental SST research and climate studies. Examples of near real time applications include hurricane and fisheries studies and numerical weather forecasting. The JPL group has produced a new 1 km daily global Level 4 SST product, the Multiscale Ultrahigh Resolution (MUR), that blends SST data from 3 distinct NASA radiometers: the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Very High Resolution Radiometer (AVHRR), and the Advanced Microwave Scanning Radiometer ? Earth Observing System(AMSRE). This new product requires further validation and accuracy assessment, especially in coastal regions.We examined the accuracy of the new MUR SST product by comparing the high resolution version and a lower resolution version that has been smoothed to 19 km (but still gridded to 1 km). Both versions were compared to the same data set of in situ buoy temperature measurements with a focus on study regions of the oceans surrounding North and Central America as well as two smaller regions around the Gulf Stream and California coast. Ocean fronts exhibit high temperature gradients (Roden, 1976), and thus satellite data of SST can be used in the detection of these fronts. In this case, accuracy is less of a concern because the primary focus is on the spatial derivative of SST. We calculated the gradients for both versions of the MUR data set and did statistical comparisons focusing on the same regions.

sea surface temperature SST)↗
NASA NTRSDate not supplied

Characterizing Wildfires in Western US.: A Cloud-based Case Study for Interdisciplinary Research using NASA Resources

This presentation will demonstrate a case study of interdisciplinary research done in the Amazon Web Services (AWS) cloud platform, in addition to in the local machine. We conduct data analysis next to data by leveraging various cloud-based data in NASA Earthdata Cloud, which are distributed by different missions/NASA Distributed Active Archive Centers (DAACs), and cloud computing resources at NASA. For instance, we directly access multiple datasets stored in the AWS Simple Storage Service (S3) buckets using a Python Jupyter notebook through a JupyterHub interface hosted in AWS (without having to download data), and conduct data analysis next to data in the cloud. We will also show how to share the research results following Open Source policy. This case study characterizes the change in wildfire events in the western United States during the past 20 years. In particular, we focus on the wildfires in California in 2021, one of the most severe wildfire years occurring in the most recent 20 years in California. We will analyze the possible causes of wildfires, such as drought conditions and climate variability, and examine the impacts of wildfires on air quality and atmospheric composition, and on land cover. We will examine the data distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), including aerosols and meteorological data from the NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), precipitation from the Global Precipitation Measurement (GPM) and Global Precipitation Climate Project (GPCP), and aerosol index from Ozone Monitoring Instrument (OMI). We also utilize the data distributed by the Physical Oceanography (PO) DAAC, such as Sea Surface Temperature (SST) data from the Group for High Resolution Sea Surface Temperature (GHRSST), and the data distributed by Land Processes (LP) DAAC, such as Normalized Difference Vegetation Index (NDVI).

Xiaohua Pan↗
NASA NTRS2018

File Specification for M2AMIP Products: Version Number - 1.0

The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) is an atmospheric reanalysis computed with the Goddard Earth Observing (EOS) System, Version 5.12.4 (GEOS) data assimilation system (Gelaro et al., 2017). To supplement the reanalysis, the GEOS General Circulation Model (GCM) used in MERRA-2 has been used to generate a 10-member ensemble of simulations, configured following the convention of the Atmospheric Model Intercomparison Project (AMIP; Gates et al., 1992). Each ensemble member was initialized using meteorological fields from a different date in November 1979. The AMIP simulations used the sea-surface temperature (SST) and sea-ice boundary conditions that were used in MERRA-2 (Bosilovich et al., 2016). This 10-member ensemble of AMIP simulations, denoted M2AMIP, is available for download in a group of self-describing files, which are documented in this office note. All data collections are provided on the same horizontal grid as MERRA-2. This grid has 576 points in the longitudinal direction and 361 points in the latitudinal direction, corresponding to a resolution of 0.625 degrees by 0.5 degrees. Although data collections are available at this grid, all fields are computed on a cubed-sphere grid with an approximate resolution of 50 km by 50 km and are then spatially interpolated to the latitude-longitude grid. There are no changes in the vertical grids used: variables are provided on either the native vertical grid of 72 model layers, or interpolated to 42 standard pressure levels. Unlike MERRA, no data collections are available at the vertical layer edges. More details on the grid are provided in Section 4. MERRA-2 introduced observation-based precipitation forcing for the land surface parameterization and the corresponding variable PRECTOTCORR in the MERRA-2 FLX (surface turbulent fluxes and related quantities) and LFO (land-surface forcing) collections (see Section 6; Reichle et al., 2017). While this variable is still available for M2AMIP, there was no observation-based forcing, making the value identical to the model derived precipitation, PRECTOT. Similarly, without data assimilation, the values for the analysis increments, D*DTANA, in the tendency and vertically integrated file collections are zero. The M2AMIP data are available for download online through the NASA Center for Climate Simulation (NCCS) DataPortal (https://portal.nccs.nasa.gov/datashare/gmao_m2amip/). Data are arranged in subdirectories based on ensemble member, followed by year and month. Control files that are compatible with the Grid Analysis and Display System (GrADS) are available in the ctl_daily and ctl_monthly directories for the hourly, three hourly, and monthly mean data. Control files for the monthly mean diurnal cycle can be found in the ctl_diurnal subdirectory within the directory for each individual ensemble member.

M2AMIP↗
NASA NTRS1982

Determination of sea surface temperatures from microwave and IR data

Microwave measurements from the Nimbus 7 SMMR were used to derive the atmospheric precipitable water, which was then used to obtain the atmospheric correction for use with AVHRR thermal IR measurements to obtain sea surface temperature (SST). The resulting SST's were compared with the NOAA operational sea surface temperature measurements, and the two sets of measurements were found to be in reasonable agreement. The average residuals between the two sets of measurements was 0.15 K with the NOAA operational SST's being slightly greater.

Rangaswamy, S.↗
NASA NTRS1994

An inquiry into the cirrus-cloud thermostat effect for tropical sea surface temperature

In this paper, we investigate the relative importance of local vs remote control on cloud radiative forcing using a cumulus ensemble model. It is found that cloud and surface radiation forcings are much more sensitive to the mean vertical motion assoicated with large scale tropical circulation than to the local SST (sea surface temperature). When the local SST is increased with the mean vertical motion held constant, increased surface latent and sensible heat flux associated with enhanced moisture recycling is found to be the primary mechanism for cooling the ocean surface. Large changes in surface shortwave fluxes are related to changes in cloudiness induced by changes in the large scale circulation. These results are consistent with a number of earlier empirical studies, which raised concerns regarding the validity of the cirrus-thermostat hypothesis (Ramanathan and Collins, 1991). It is argued that for a better understanding of cloud feedback, both local and remote controls need to be considered and that a cumulus ensemble model is a powerful tool that should be explored for such purpose.

Lau, K.-M.↗
NASA NTRS1982

Aircraft measurement of sea surface temperature during the West Coast Experiment

During the West Coast Experiment in March 1977, a test was conducted to ascertain the effectiveness of using remote sensing techniques to estimate sea surface temperature (SST) from infrared (IR) emissions of the sea surface. Aircraft flights were made over three buoys moored in southern California coastal waters, and data was collected of sea surface emissions at thermal IR wavelengths (7.95-13.5 micrometers). SST obtained from the remote sensing measurements were compared with in situ SST measured with thermistors mounted on the buoys. The remotely determined SST were from 1.4-2.9 C lower than the in situ measurements. Several factors are discussed that could account for the differences.

Schieldge, J. P.↗
NASA NTRS2001

Evaporation/SST Sensitivity Over the Tropical Oceans During ENSO Events as Estimated from the da Silva, Young, Levitus Surface Marine Data Set

The da Silva, Young and Levitus Surface Marine Atlas, based on observations from the Comprehensive Ocean Atmosphere Data Set (COADS) Release 1, has been used to investigate the relationship between evaporation and sea-surface temperature (SST) over the global oceans. For the period 1950 to 1987 SST, surface latent heat flux, and other related variables have been filtered to minimize data uncertainties and to focus upon interannual variations associated with warm (El Nino) and cold (La Nina) ENSO events. Compositing procedures have enabled identification of systematic variations in latent heat fluxes accompanying these events and the relationship to spatial anomalies in ocean surface wind speed and humidity. The evaporation response associated with ENSO sea surface temperature (SST) variability is systematic in nature and composed of offsetting contributions from the surface wind and humidity variations. During warm events exceeding 1.0 S.D. delta SST, increases in the surface humidity deficit, delta(qs-qa), between the surface and 2m height dominate regions of positive SST anomalies and lead to increases in evaporation of almost 2 Wm (exp -2) at deltaSST = 0.23 K. Despite the increases in specific humidity, relative humidity decreases slightly in regions of elevated SSTs. For the most part, variations in wind speed are consistent with previous investigations. Weakening of the equatorial easterlies (and generation of westerlies) between 160 degrees E and 140 degrees W dominates during the early phases of warm events. Elevated wind speeds in adjacent subtropical regions and in the eastern equatorial Pacific subsequently develop too. The net contribution of these winds, which reflect adjustments in Hadley and Walker circulation components is toward reduced evaporation. Results for cold periods are approximately similar, but opposite in sign to warm events, though evidence of different temporal evolution is noted.

Robertson, F. R.↗
NASA NTRS1975

Atmospheric response to variations in sea-surface temperature

A two-week prediction experiment was performed with the GISS atmospheric model on a global data set beginning 20 December 1972 to test the sensitivity of the model to sea-surface temperature (SST) variations. Use of observed SST's in place of climatological monthly mean sea temperatures for surface flux calculations in the model was found to have a marked local effect on predicted precipitation over the ocean, with enhanced convection over warm SST anomalies. However, use of observed SST's did not lead to any detectable general improvement in forecast skill. The influence of the SST anomalies on daily predicted fields of pressure and geopotential was small up to about one week compared with the growth of prediction error, and no greater over a two-week period than that resulting from random errors in the initial meteorological state. The 14-day average fields of sea-level pressure and 500 mb height predicted by the model were similarly insensitive to the SST anomalies.

Spar, J.↗
NASA NTRS2011

NASA SPoRT Initialization Datasets for Local Model Runs in the Environmental Modeling System

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed several products for its National Weather Service (NWS) partners that can be used to initialize local model runs within the Weather Research and Forecasting (WRF) Environmental Modeling System (EMS). These real-time datasets consist of surface-based information updated at least once per day, and produced in a composite or gridded product that is easily incorporated into the WRF EMS. The primary goal for making these NASA datasets available to the WRF EMS community is to provide timely and high-quality information at a spatial resolution comparable to that used in the local model configurations (i.e., convection-allowing scales). The current suite of SPoRT products supported in the WRF EMS include a Sea Surface Temperature (SST) composite, a Great Lakes sea-ice extent, a Greenness Vegetation Fraction (GVF) composite, and Land Information System (LIS) gridded output. The SPoRT SST composite is a blend of primarily the Moderate Resolution Imaging Spectroradiometer (MODIS) infrared and Advanced Microwave Scanning Radiometer for Earth Observing System data for non-precipitation coverage over the oceans at 2-km resolution. The composite includes a special lake surface temperature analysis over the Great Lakes using contributions from the Remote Sensing Systems temperature data. The Great Lakes Environmental Research Laboratory Ice Percentage product is used to create a sea-ice mask in the SPoRT SST composite. The sea-ice mask is produced daily (in-season) at 1.8-km resolution and identifies ice percentage from 0 100% in 10% increments, with values above 90% flagged as ice.

Case, Jonathan L.↗
NASA NTRS2006

Relationships of Upper Tropospheric Water Vapor, Clouds and SST: MLS Observations, ECMWF Analyses and GCM Simulations

The relationships of upper tropospheric water vapor (UTWV), cloud ice and sea surface temperature (SST) are examined in the annual cycles of ECMWF analyses and simulations from 15 atmosphere-ocean coupled models which were contributed to the IPCC AR4. The results are compared with the observed relationships based on UTWV and cloud ice measurements from MLS on Aura. It is shown that the ECMWF analyses produce positive correlations between UTWV, cloud ice and SST, similar to the MLS data. The rate of the increase of cloud ice and UTWV with SST is about 30% larger than that for MLS. For the IPCC simulations, the relationships between UTWV, cloud ice and SST are qualitatively captured. However, the magnitudes of the simulated cloud ice show a considerable disagreement between models, by nearly a factor of 10. The amplitudes of the approximate linear relations between UTWV, cloud ice and SST vary by a factor up to 4.

sea surface temperature (SST)↗
NASA NTRS1974

Atmospheric response to variations in sea surface temperature

An extended range prediction experiment was performed with the GISS atmospheric model on a global data to test the sensitivity of the model to sea surface temperature (SST) variation over a two-week forecast period. The use of an initial observed SST field in place of the climatological monthly mean sea temperatures for surface flux calculations in the model was found to have a significant effect on the predicted precipitation over the ocean, with enhanced convection computed over areas where moderately large warm SST anomalies are found. However, there was no detectable positive effect of the SST anomaly field on forecast quality. The influence of the SST anomalies on the daily predicted fields of pressure and geopotential is relatively insignificant up to about one week compared with the growth of prediction error, and is no greater over a two-week period than that resulting from random errors in the initial meteorological state. The 14-day average fields of sea level pressure and 500-mb height predicted by the model, appear to be similarly insensitive to anomalies of sea surface temperature.

Spar, J.↗
NASA NTRS1997

The Onset of the Madden-Julian Oscillation Within an Aquaplanet Model

A series of numerical experiments using a two-level atmospheric general circulation model (AGCM) were performed for the purpose of investigating the coupling between sea surface temperature (SST) profile and the onset of the Madden-Julian Oscillation (MJO). The AGCM was modified to run as an aquaplane with all seasonal forcing removed. SST distributions based on the New Global Sea-Ice and Sea Surface Temperature (GISST) Data Set for 1903-1994 were generated then modified to vary the north-south gradient and tropical temperatures. It was found that the MJO signal did not depend on the SST temperature gradients but rather on the absolute temperature of the equatorial region, EOF analysis revealed that the SST distribution which generated the strongest MJO signal produced a periodic fluctuation in velocity potential at the 250 millibar level with a phase speed of 15 m/s, and a periodicity of 30 days which falls within the shortest limit of observed oscillations. This distribution also possessed the coolest equatorial SSTs which suggests that increased stability in the atmosphere favors the occurrence of organized MJO propagation.

Colon, Edward↗
NASA NTRS2023

Explainable deep learning for insights in El Niño and river flows

The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.

SST↗
NASA NTRS2012

Recent Upgrades to NASA SPoRT Initialization Datasets for the Environmental Modeling System

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed several products for its National Weather Service (NWS) partners that can initialize specific fields for local model runs within the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS). In last year's NWA abstract on this topic, the suite of SPoRT products supported in the STRC EMS was presented, which includes a Sea Surface Temperature (SST) composite, a Great Lakes sea-ice extent, a Green Vegetation Fraction (GVF) composite, and NASA Land Information System (LIS) gridded output. This abstract and companion presentation describes recent upgrades made to the SST and GVF composites, as well as the real-time LIS runs. The Great Lakes sea-ice product is unchanged from 2011. The SPoRT SST composite product has been expanded geographically and as a result, the resolution has been coarsened from 1 km to 2 km to accommodate the larger domain. The expanded domain covers much of the northern hemisphere from eastern Asia to western Europe (0 N to 80 N latitude and 150 E to 10 E longitude). In addition, the NESDIS POES-GOES product was added to fill in gaps caused by the Moderate Resolution Imaging Spectroradiometer (MODIS) being unable to sense in cloudy regions, replacing the recently-lost Advanced Microwave Scanning Radiometer for EOS with negligible change to product fidelity. The SST product now runs twice per day for Terra and Aqua combined data collections from 0000 to 1200 UTC and from 1200 to 0000 UTC, with valid analysis times at 0600 and 1800 UTC. The twice-daily compositing technique reduces the overall latency of the previous version while still representing the diurnal cycle characteristics. The SST composites are available at approximately four hours after the end of each collection period (i.e. 1600 UTC for the nighttime analysis and 0400 UTC for the daytime analysis). The real-time MODIS GVF composite has only received minor updates in the past year. The domain was expanded slightly to extend further west, north, and east to improve coverage over parts of southern Canada. Minor adjustments were also made to the manner in which GVF is calculated from the distribution of maximum Normalized Difference Vegetation Index from MODIS. The presentation will highlight some examples of the substantial inter-annual change in GVF that occurred from 2010 to 2011 in the U.S. Southern Plains as a result of the summer 2011 drought, and the early vegetation green up across the eastern U.S. due to the very warm conditions in March 2012. Finally, the SPoRT LIS runs the operational Noah land surface model (LSM) in real time over much of the eastern half of the CONUS. The Noah LSM is continually cycled in real time, uncoupled to any model, and driven by operational atmospheric analyses over a long-term, multi-year integration. The LIS-Noah provides the STRC EMS with high-resolution (3 km) LSM initialization data that are in equilibrium with the operational analysis forcing. The Noah LSM within the SPoRT LIS has been upgraded from version 2.7.1 to version 3.2, which has improved look-up table attributes for several land surface quantities. The surface albedo field is now being adjusted based on the input real-time MODIS GVF, thereby improving the net radiation. Also, the LIS-Noah now uses the newer MODIS-based land use classification scheme (i.e. the International Biosphere-Geosphere Programme [IGBP]) that has a better depiction of urban corridors in areas where urban sprawl has occurred. STRC EMS users interested in initializing their LSM fields with high-resolution SPoRT LIS data should set up their model domain with the MODIS-IGBP 20-class land use database and select Noah as the LSM.

Case, Jonathan L.↗
NASA NTRS2015

Enhanced Pacific Ocean Sea Surface Temperature and Its Relation to Typhoon Haiyan

Typhoon Haiyan, which devastated the Visayan Islands in the Philippines on November 8, 2013 was recorded as the strongest typhoon ever-observed using satellite data. Typhoons in the region usually originate from the mid-Pacific region that includes the Warm Pool, which is regarded as the warmest ocean surface region globally. Two study areas were considered: one in the Warm Pool Region and the other in the West Pacific Region near the Philippines. Among the most important factors that affect the strength of a typhoon are sea surface temperature (SST) and water vapor. It is remarkable that in November 2013 the average SST in the Warm Pool Region was the highest observed during the 1981 to 2014 period while that of the West Pacific Region was among the highest as well. Moreover, the increasing trend in SST was around 0.20C per decade in the warm pool region and even higher at 0.23C per decade in the West Pacific region. The yearly minimum SST has also been increasing suggesting that the temperature of the ocean mixed layer is also increasing. Further analysis indicated that water vapor, clouds, winds and sea level pressure for the same period did not reveal strong signals associated with the 2013 event. The SST is shown to be well-correlated with wind strength of historically strong typhoons in the country and the observed trends in SST suggest that extremely destructive typhoons like Haiyan are likely to occur in the future.

warm pool↗
NASA NTRS1972

Supplementary notes on sea surface temperature anomalies and model-generated meteorological histories

In seasonal computations, the Mintz-Arakawa two-level model is found to be sensitive to a minor alteration in the computational program. Effects of the program change on monthly mean sea level pressure fields are small in the first month, but large in the second and third months, although the meteorological histories generated by both the original and modified programs are equally credible. The inherited effects of a transient (one month) sea surface temperature (SST) anomaly on the computed monthly mean sea level pressure fields over a period of a season are about as large in absolute magnitude as those generated in the model by a persistent (seasonal) SST anomaly. The effects of a transient SST anomaly in the North Pacific Ocean on monthly and seasonal temperature and precipitation in the eastern United States may be large enough to produce a change of one or two class intervals in these predicted weather elements. The model-generated precipitation in the equatorial region is also found to be sensitive to the sea surface temperature field in the North Pacific.

Spar, J.↗