Sentences with phrase «with sea surface temperature data»

The study compares detailed daily observations of cloud cover from Japan's GMS - 5 Geostationary Meteorological Satellite with sea surface temperature data from the U. S. National Weather Service's National Centers for Environmental Prediction over a 20 - month period (January 1998 to August 1999).

To develop the model, they compared historic fire data from NASA's Terra satellite with sea surface temperature data in the tropical Pacific and North Atlantic oceans from buoys and satellite images compiled by the National Oceanic and Atmospheric Administration.

They combined previously - collected penguin population data from 1982 to 2014 with sea surface temperature data from satellites, ships and buoys for the same time period.

Not exact matches

One of the challenges has been accurately determining the difference between sea surface temperatures at the poles and the equator during the Eocene, with models predicting greater differences than data suggested.

Analyzing data collected over a 20 - month period, scientists from NASA's Goddard Space Flight center in Greenbelt, Md., and the Massachusetts Institute of Technology found that the number of cirrus clouds above the Pacific Ocean declines with warmer sea surface temperatures.

«In our study we used satellite data for sea ice and sea surface temperatures to run some coordinated hindcast experiments with five different atmospheric models,» Ogawa says.

The first image, based on data from January 1997 when El Nio was still strengthening shows a sea level rise along the Equator in the eastern Pacific Ocean of up to 34 centimeters with the red colors indicating an associated change in sea surface temperature of up to 5.4 degrees C.

I found problems with the data including: ««⠉ NOAA buoys measuring near - to - sea - surface air temperature — e.g. inadequate shielding of direct solar heating ««⠉ ship - based sea surface temperature — e.g. variable points in cooling systems for diesel versus steam ship propulsion

A well - known issue with LGM proxies is that the most abundant type of proxy data, using the species composition of tiny marine organisms called foraminifera, probably underestimates sea surface cooling over vast stretches of the tropical oceans; other methods like alkenone and Mg / Ca ratios give colder temperatures (but aren't all coherent either).

The red line shows the observations (HadCRU3 data), the black line a standard IPCC - type scenario (driven by observed forcing up to the year 2000, and by the A1B emission scenario thereafter), and the green dots with bars show individual forecasts with initialised sea surface temperatures.

Like almost all historical climate data, ship - board sea surface temperatures (SST) were not collected with long term climate trends in mind.

Using monthly - averaged global satellite records from the International Satellite Cloud Climatology Project (ISCCP [5]-RRB- and the MODerate Resolution Imaging Spectroradiometer (MODIS) in conjunction with Sea Surface Temperature (SST) data from the National Oceanic and Atmospheric (NOAA) extended and reconstructed SST (ERSST) dataset [7] we have examined the reliability of long - term cloud measurements.

One thing I would have liked to see in the paper is a quantitative side - by - side comparison of sea - surface temperatures and upper ocean heat content; all the paper says is that only «a small amount of cooling is observed at the surface, although much less than the cooling at depth» though they do report that it is consistent with 2 - yr cooling SST trend — but again, no actual data analysis of the SST trend is reported.

Here we analyze a series of climate model experiments along with observational data to show that the recent warming trend in Atlantic sea surface temperature and the corresponding trans - basin displacements of the main atmospheric pressure centers were key drivers of the observed Walker circulation intensification, eastern Pacific cooling, North American rainfall trends and western Pacific sea - level rise.

Canadian Ice Service, 4.7, Multiple Methods As with CIS contributions in June 2009, 2010, and 2011, the 2012 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter arctic ice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoSea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictosea ice predictors.

In a study published in the journal Nature the researchers say analysis of sea surface temperature data shows that the AMOC has slowed down by roughly 15 % since the middle of the 20th century, with human - made climate change a prime suspect.

A known problem with that dataset is that GISS Deletes Arctic And Southern Ocean Sea Surface Temperature (SST) Data.

It's hard to imagine how Cowtan and Way could determine with any degree of certainty how «the hybrid method works best over land and most importantly sea ice» when there is so little surface air temperature data over sea ice.

In the main part of the paper, for China, we compare a new homogenized station data set with gridded temperature products and attempt to assess possible urban influences using sea surface temperature (SST) data sets for the area east of the Chinese mainland.

Investigators outside NOAA are finding interesting trends and showing that they seem to be correlated with trends in such variables as SST [Sea Surface Temperature] in key regions, the changes of which almost certainly are due to human - induced changes in the climate, though having enough data to get all the statistics right is often problematic.

with corresponding sea surface temperature data, will be downloaded and plotted.

Give the students the graph below from Johnstone 2014 and ask them to compare changes in sea surface temperatures (SST in red) with the raw and recently homogenized temperature data from southern California.

Now with a slightly different group, Li has compiled data from multiple sources and performed model simulations to investigate the possibility that Arctic stratospheric ozone is connected to the ENSO via the North Pacific sea - surface temperature (SST).

What we know with some certainty about oceans (if data is to be believed) is that the intra-annual change in the insolation effects (suspiciously) high symmetricity in the N. Atlantic's sea surface temperature, cantered on 1st of March and 31st of August.

That is, the animation of the GISS maps and the data GISS provides with those maps show that the trends in global sea surface temperature are driven by the multidecadal variations in the strengths and magnitudes of El Niño and La Niña events.

Canadian Ice Service; 5.0; Statistical As with Canadian Ice Service (CIS) contributions in June 2009 and June 2010, the 2011 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic Multi-Year Ice (MYI) extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predictoSea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predictosea ice predictors.

Any discussion on that webpage you linked... https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php... regarding their preference for anomalies has to do with land surface, not sea surface, temperatures, which is why their land surface temperature data and consequently their combined land + ocean data are presented as anomalies.

Because the model - simulated sea surface temperatures are too warm globally, this shortcut helps to better align the data with the models.

Until recently, a systematic ocean data collection did not exist, with the exception of the frequent sampling of the sea surface temperatures made by merchant vessels.

Because the GISS analysis combines available sea surface temperature records with meteorological station measurements, we test alternative choices for the ocean data, showing that global temperature change is sensitive to estimated temperature change in polar regions where observations are limited.

It has been noted by investigators that the algorithms used for adjusting satellite observed SST data has been inconsistent, cloud coverage has limited the adequacy of satellite coverage, and in - situ measurements by VOS and buoy networks has been inadequate with respect to the datasets produced by the Advanced Very High Resolution Radiometers (AVHRR), Cross Product Sea Surface Temperature (CPSST), Non-Linear SST (NLSST), and Multi-Channel Sea Surface Temperature (MCSST) methods.

«Causes of differences in model and satellite tropospheric warming rates» «Comparing tropospheric warming in climate models and satellite data» «Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures» «Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends» «Reconciling warming trends» «Natural variability, radiative forcing and climate response in the recent hiatus reconciled» «Reconciling controversies about the «global warming hiatus»»

If Karl was trying to come up with an accurate sea surface temperature dataset, he should have thrown out the inaccurate ship data instead.

Figure 3: Global mean sea level variations (light line) computed from the TOPEX / POSEIDON satellite altimeter data compared with the global averaged sea surface temperature variations (dark line) for 1993 to 1998.

The issue is with bad data, as Dr. Pat Michaels Dr. Richard Lindzen, and Dr. Chip Knappenberger observed related to the switch from buckets on a rope to engine water inlets for measuring sea surface temperature:

We also generated current and projected future temperature maps, which we compared with sea surface temperature (SST) data from the 1980s.

While derived from sea surface temperature data, the PDO index is well correlated with many records of North Pacific and Pacific Northwest climate and ecology, including sea level pressure, winter land — surface temperature and precipitation, and stream flow.

Note we're using BEST land area, so actual rates of warming are slightly elevated from global levels including sea surface temperatures, however BEST has enough resolution to allow us to work with 12.5 years of temperature data and not have such abysmal CI as to need to reject the comparisons outright..

As with previous CIS contributions, the 2016 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness / extent, as well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

Now the NOAA data comes in and confirms the GISS data, and shows the http://www.ncdc.noaa.gov/oa/climate/research/2009/jun/global.html Global Highlights: Based on preliminary data, the globally averaged combined land and sea surface temperature was the second warmest on record for June and the January - June year - to - date tied with 2004 as the fifth warmest on record.

Scientists at NASA's Goddard Institute for Space Studies (GISS) in New York carried out their analysis by collating data from about 6,300 meteorological stations around the world, along with instruments that track sea surface temperatures and Antarctic research stations.

aaron, all three datasets start with the same source data: land surface air temperatures and sea surface temperatures.

2) The satellite tropospheric and sea surface (SST) data differ from the HADCRUT surface temp anomaly, with the present temperatures of both right at the same level as in 1991 (while Fig. 1 here shows an increase over 1991 of about 0.25 °C).

Starting with their January 2013 update, it uses NCDC ERSST.v3b sea surface temperature data.

This is likely caused, in part, by GISS masking sea surface temperature data in the polar oceans and replacing it with land surface air temperature data, which is naturally more volatile.

Moreover, taking the proxy sea surface temperature data for the peak Eocene period (55 — 48 Myr BP) at face value yields a global temperature of 33 — 34 °C (fig. 3 of Bijl et al. [84]-RRB-, which would require an even larger CO2 amount with the same climate models.

Hausfather separated out these different records and compared them with independent data from other sources, including satellites and robotic floats that also measured sea surface temperatures.

Pictured above is the East Coast of the United States, in grey, with the Gulf Stream, in yellow and orange, revealed through Sea Surface Temperature data (SST), made from the MODIS instrument on the Terra satellite.

«Adjustments are largely to sea surface temperatures (SST) and appear to align ship measurements of SST with night marine air temperature (NMAT) estimates, which have their own data bias problems.

Combined with data from satellites, the Global Drifter Network now provides scientists with twice - weekly updates on currents and sea surface temperatures throughout the world.