Sentences with phrase «mean surface temperature data»

NASA released their 2016 global mean surface temperature data today.

Not exact matches

The analysis of high - frequency surface air temperature, mean sea - level pressure, wind speed and direction and cloud - cover data from the solar eclipse of 20 March 2015 from the UK, Faroe Islands and Iceland, published today (Monday 22 August 2016), sheds new light on the phenomenon.

However, comparison of the global, annual mean time series of near - surface temperature (approximately 0 to 5 m depth) from this analysis and the corresponding SST series based on a subset of the International Comprehensive Ocean - Atmosphere Data Set (ICOADS) database (approximately 134 million SST observations; Smith and Reynolds, 2003 and additional data) shows a high correlation (r = 0.96) for the period 1955 to 2Data Set (ICOADS) database (approximately 134 million SST observations; Smith and Reynolds, 2003 and additional data) shows a high correlation (r = 0.96) for the period 1955 to 2data) shows a high correlation (r = 0.96) for the period 1955 to 2005.

When differences in scaling between previous studies are accounted for, the various current and previous estimates of NH mean surface temperature are largely consistent within uncertainties, despite the differences in methodology and mix of proxy data back to approximately A.D. 1000... Conclusions are less definitive for the SH and globe, which we attribute to larger uncertainties arising from the sparser available proxy data in the SH.

Does that mean the global mean surface temperature trends over the 20th Century, or just that some 20th Century data is used?

More than 95 % of the 5 yr running mean of the surface temperature change since 1850 can be replicated by an integration of the sunspot data (as a proxy for ocean heat content), departing from the average value over the period of the sunspot record (~ 40SSN), plus the superimposition of a ~ 60 yr sinusoid representing the observed oceanic oscillations.

Large variability reduces the number of new records — which is why the satellite series of global mean temperature have fewer expected records than the surface data, despite showing practically the same global warming trend: they have more short - term variability.

Indeed, there's a world of difference between citing one paper that has done something that MIGHT rebalance the global mean temperature data — as Joe's post suggests — and then assuming that the problem is fixed and the indicator remains the first best only way to measure global goals despite the fact that natural variability in the global mean surface temperature will also make that a sluggish measure.

But I would suppose that equilibrium climate sensitivity [background] and even global mean surface temperature on a decadal scale could be better nailed down by model pruning and better ocean data.

The AARI data include drifting stations and ice information, although not the majority (my fault to see that as «main»), that means that the difference between only land based and total is in warmer sea surface temperatures.

«The average global temperature anomaly for combined land and ocean surfaces for July (based on preliminary data) was 1.1 degrees F (0.6 degrees C) above the 1880 - 2004 long - term mean.

Given that, here are the absolute global mean surface temperatures in five reanalysis products (ERAi, NCEP CFSR, NCEP1, JRA55 and MERRA2) since 1980 (data via WRIT at NOAA ESRL).

The code currently starts from the annual - mean data for the surface, upper - air, and deep - ocean temperatures that were extracted from the MIT IGSM model output files.

As a result, directly comparing the Sea Surface Temperature data from the early 20th century to the current Sea Surface Temperature data is like «comparing apples and oranges» — there have been too many changes in the data sources for such comparisons to have much meaning.

However, comparison of the global, annual mean time series of near - surface temperature (approximately 0 to 5 m depth) from this analysis and the corresponding SST series based on a subset of the International Comprehensive Ocean - Atmosphere Data Set (ICOADS) database (approximately 134 million SST observations; Smith and Reynolds, 2003 and additional data) shows a high correlation (r = 0.96) for the period 1955 to 2Data Set (ICOADS) database (approximately 134 million SST observations; Smith and Reynolds, 2003 and additional data) shows a high correlation (r = 0.96) for the period 1955 to 2data) shows a high correlation (r = 0.96) for the period 1955 to 2005.

Unfortunately, we don't have good ocean heat content data for this period, while the data we do have — global mean atmospheric surface temperature — is dominated by ocean oscillations.

In the Comment by Nuccitelli et al., they make many false and invalid criticisms of the CFC - warming theory in my recent paper, and claim that their anthropogenic forcings including CO2 would provide a better explanation of the observed global mean surface temperature (GMST) data over the past 50 years.

A similar mismatch between LIG - 120 mean annual surface temperature (MAT) simulation and proxy data is also described by Otto - Bliesner et al. 21.

Daily mean NCEP / NCAR reanalysis data are used as atmospheric forcing, i.e., 10 - m surface winds, 2 - m surface air temperature (SAT), specific humidity, precipitation, evaporation, downwelling longwave radiation, sea level pressure, and cloud fraction.

In regards the gridded network» stations, I have been informed that the Climate Research Unit's (CRU) monthly mean surface temperature dataset has been constructed principally from data available on the two websites identified in my letter of 12 March 2007.

All data are shown as global mean temperature anomalies relative to the period 1901 to 1950, as observed (black, Hadley Centre / Climatic Research Unit gridded surface temperature data set (HadCRUT3); Brohan et al., 2006) and, in (a) as obtained from 58 simulations produced by 14 models with both anthropogenic and natural forcings.

The fit of a trend line to the time series of global - mean surface temperature (e.g., Figure 2.5) indicates a warming between 0.25 to 0.4 °C for this 20 - year period, or approximately 0.1 to 0.2 °C per decade, 6 depending upon which of the existing data sets is used to represent the surface temperatures, and exactly how the fitting is done.

Time series of seasonally averaged global surface temperature (December 1879 — August 1999) based on the Quayle et al. (1999) data set, computed as differences from the 1880 — 1998 mean.

And I should add to the last post that by global warming I mean increases in the global surface temperature, which is certainly not the only climate metric, or necessary the best one, but is the one for which we have the best data.

Dating back into the late nineteenth century, the data coverage has been dense enough to reveal the existence of gradual changes in hemispheric - and global - mean surface temperature.

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.

Kevin C's excellent trend tool shows us what the new data mean for the surface temperature trend since 1970: it's about +0.17 C per decade, but there's a range in that because short term wiggles are caused by things like the El Nino - La Nina cycle in the Pacific which warm or cool the atmosphere by storing or releasing heat from the oceans.

The range (due to different data sets) of the global mean tropospheric temperature trend since 1979 is 0.12 °C to 0.19 °C per decade based on satellite - based estimates (Chapter 3) compared to a range of 0.16 °C to 0.18 °C per decade for the global surface warming.

Omission of successively larger polar regions from the global - mean temperature calculations, in both tropospheric and surface data sets, shows that data gaps at high latitudes can not explain the observed differences between the hiatus and the pre-hiatus period....

TCR (1 + beta) extracted from HadCRUT4 data since 1850 is 1.8 C and only has the uncertainty of the global mean surface temperature measurement that you argue in Lewis and Curry (2014) is insignificant compared to the aerosol contribution uncertainty.

Now, researchers from Germany and the US, who examined global mean surface temperature (GMST) trends in the light of a recent series of three record - breaking years in a row in most data sets, have published the results of their study, which identified two important pitfalls in analysing GMST trends, in Environmental Research Letters.

To achieve an average surface air temperature, or a global mean temperature, first establish a baseline for the measurements; and then weigh new data against the base line.

We blended surface meteorological observations, remotely sensed (TRMM and NDVI) data, physiographic indices, and regression techniques to produce gridded maps of annual mean precipitation and temperature, as well as parameters for site - specific, daily weather generation for any location in Yemen.

But the heart of his paper is the construction from published metereological data of a table of mean temperature and relative and absolute humidity for the surface of the earth between 60 degrees south and 70 degrees north.

The scientists determined their findings by using data — 5.1 million temperature profiles — from sources around the world, to quantify the variability of the heat content (mean temperature) of the world ocean from the surface through 3000 meter depth for the period 1948 to 1996.

I don't think these new results will in any case affect the yearly mean temperature grid calculations, as they depend on actual surface station temperature records — which both we and Steig et al. used — and not on reconstructed gridded data.

Global average temperature The mean surface temperature of the Earth measured from three main sources: satellites, monthly readings from a network of over 3,000 surface temperature observation stations and sea surface temperature measurements taken mainly from the fleet of merchant ships, naval ships and data buoys.

GISS relies on data collected by other organizations, specifically, NOAA / NCEI's Global Historical Climatology Network (GHCN) v3 adjusted monthly mean data as augmented by Antarctic data collated by UK Scientific Committee on Antarctic Research (SCAR) and also NOAA / NCEI's Extended Reconstructed Sea Surface Temperature (ERSST) v5 data.

Antartica would contribute a bit under 9.5 % of the mean global land surface temperature and a bit under 2.8 % of the mean global surface temperature, if I have got my data right.

The global mean of the local standard deviation of June — July — August surface temperature increases from 0.50 °C for 1951 — 980 data to 0.58 °C for 1981 — 2010 data.

The monthly global surface temperature data are from NCDC, NOAA: http://www.ncdc.noaa.gov/oa/climate/research/anomalies/index.html; the global mean sea level data are from AVISO satellite altimetry data: http://www.aviso.oceanobs.com/en/news/ocean-indicators/mean-sea-level/; and the CO2 at Mauna Loa data are from NOAA http://www.esrl.noaa.gov/gmd/ccgg/trends/

To create the CRUTEM surface temperature analysis, CRU scientists take temperature data from 4,138 stations, and for each station they calculate the mean temperature for 1961 - 1990 and temperature anomalies relative to that period.

In fact, scientists are finding that, more recently, tree ring proxy data for current growth is diverging from surface temperature data, meaning either that surface temperature data is flawed or that they don't really understand how to scale tree ring data yet.

The space - time structure of natural climate variability needed to determine the optimal fingerprint pattern and the resultant signal - to - noise ratio of the detection variable is estimated from several multi-century control simulations with different CGCMs and from instrumental data over the last 136 y. Applying the combined greenhouse gas - plus - aerosol fingerprint in the same way as the greenhouse gas only fingerprint in a previous work, the recent 30 - y trends (1966 — 1995) of annual mean near surface temperature are again found to represent a significant climate change at the 97.5 % confidence level.