Sentences with phrase «giss dataset»

Both the RCP8.5 and GISS dataset anomalies used in above chart were calculated by KNMI using the 1981 - 2010 span as the baseline.

The leading NASA GISS dataset shows the years 1998, 2002, 2003, 2006, 2007 and 2009 are statistically tied for third warmest year — with 2007 measurements the highest of these six.

Source: IPCC AR5, data from the HadCRUT4 dataset (black), UK Met Office Hadley Centre, the NCDC MLOST dataset (orange), US National Oceanic and Atmospheric Administration, and the NASA GISS dataset (blue), US National Aeronautics and Space Administration.

Yet when Brandon plotted his GISS dataset that warming trend disappears.

Pending a complete examination, we can not know what other errors the GISS dataset might contain.

Near the end of the URL of the raw GISS dataset under discussion you find the following:

This means simply that until GISS are able to demonstrate a sound scientific foundation for their capricious and arbitrary adjustments, we can not trust the final GISS dataset.

Why doesn't the GISS dataset comport with climate reality as documented by other sources and experts?

Both these datasets which in spite of being far more precise than the surfaced based data (because the physical biases of sampling are not present to the same extent in the satellite data), the HadCRUT3 dataset, the NCDC dataset and even the GISS dataset all are more or less consistent with the two satellite based datasets.

If we look at the GISS dataset (I'm using [raw GHCN + USHCN corrections] at the moment) as a matrix of year - months x stations, how should one go about getting the data into a single global average annual series, given that there's so many missing values?

In the 2007 analysis of the GISS dataset, Detroit Lakes was used as a test case.

The red line shows predicted temperature change for the current level of solar activity, the blue line shows predicted temperature change for solar activity at the much lower level of the Maunder Minimum, and the black line shows observed temperatures from the NASA GISS dataset through 2010.

This would qualify the GISS dataset as non-homogonous and therefore worthless.

We'll use a GISS dataset that represents Stratospheric Aerosols.

I regularly speak to public audiences about climate change (see http://www.andrewgunther.com/climate-change/#talks for details), and use the NASA / GISS dataset to discuss global average temperature of the atmosphere.

At any rate, the GISS dataset * shows 2007 was second - warmest on record.

But even with the recent dip, January 2008 was still higher than almost every month from 1980 back to the beginning of records (in the GISS dataset at least).

When using the GISS dataset, the previous two intervals are similar to the CRU data, but the most recent is higher, resulting in the» 78 - ’05 interval being lower than the present.

I therefore applied this method to the gridded CRU and GISS datasets to produce a like - for - like comparison.

I originally stated that Brandon Shollenberger did something wrong in his analysis, which was very obvious to anyone that can lift a finger and compare for themselves the BEST and GISS datasets.

Moreover, if we compare the actual Springfield BEST and GISS datasets, we see that they align very closely.

GISS datasets are used by the UN Intergovernmental Panel on Climate Change (IPCC) to document global warming.

Yet amazingly, knowing all this, stations like this, and stations that have instrumental biases such as Tucson, with its parking lot placement (USHCN) and HO83 problems (GISS) still remain as part of the USHCN and GISS datasets.

Not exact matches

Figure 2: The data (green) are the average of the NASA GISS, NOAA NCDC, and HadCRUT4 monthly global surface temperature anomaly datasets from January 1970 through November 2012, with linear trends for the short time periods Jan 1970 to Oct 1977, Apr 1977 to Dec 1986, Sep 1987 to Nov 1996, Jun 1997 to Dec 2002, and Nov 2002 to Nov 2012 (blue), and also showing the far more reliable linear trend for the full time period (red).

Dogmatism, toxicology, linear no - dose threshold, LNT, Calabrese, EPA, Watergate II, sea level rise, IPCC, datasets calibrated, Mann, NCEI, GISS, cryosphere, GRACE, mountain glaciers, ocean expansion, polar ice caps, DMI, sun, bias, 2 billion, gamble

Figure 7: a, b d) plots of global temperature in degrees C since 1850 from Hadcrut, GISS, and Berkeley combined land and ocean datasets.

But just over a year earlier, from just February to May in 2006 saw a drop of 0.46 C in the UAH dataset (only a 0.14 C drop in GISS).

When calculating the differences between the dataset of the year 1997 and the year 2016 (annual data to avoid «cherry picking») from 1880 on you get the result that 22 % of the trendslope 1880 to 1996 is due to the changes in the GISS data.

In your preferred dataset it is.5, in the GISS analysis it is.45 Re Lindzen's point, what do you think he means by «climate internal variability»?

The UAH satellite data, however, shows less than half the warming of the smallest of the surface datasets (GISS), about 40 % of the Jones warming, and about a quarter of the GHCN warming.

If Bob used a different dataset instead of GISS that did nt have the problem he worried about (deletion of some SST) would that be better?

LET»S LOOK AT THE TROPICS AND SOUTHERN HEMISPHERE The Southern Hemisphere and Tropics dataset includes the GISS LOTI data from 60S - 20N, Figure 14.

In our analysis we use eight well - known datasets: 1) globally averaged well - mixed marine boundary layer CO2 data, 2) HadCRUT3 surface air temperature data, 3) GISS surface air temperature data, 4) NCDC surface air temperature data, 5) HadSST2 sea surface temperature data, 6) UAH lower troposphere temperature data series, 7) CDIAC data on release of anthropogene CO2, and 8) GWP data on volcanic eruptions.

What interests me is that the satellite datasets show lower lows for May, something which can't be said for either GISS or Hadley.

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

REPLY: Yes, GISS only updates this particular dataset on annual boundaries.

But in the concluding remark Bob you say: «When the two GISS LOTI datasets were again combined, we had removed approximately 85 % of what some consider to be the «anthropogenic global warming signal.»

The similarities between the adjusted GISS LOTI datasets and the respective KOE and SPCZ Extension data were remarkable.

Unlike GISS and NCDC global surface temperature datasets, HADCRUT4 data are not infilled.

calculations can be made for each subsequent month (GISS / NASA monthly dataset commences at January 1880).

In 1956, the average global surface temperature anomaly in the three datasets (NASA GISS, NOAA NCDC, and HadCRUT4) was -0.21 °C.

In CRUTEM4: A detailed look, I pointed out the difficulties in providing a comparison of the CRUTEM4 data with the other land - only temperature datasets from NCDC, GISS or BEST due to problems created by different definitions of «land - only», and different averaging and baseline conventions.

I have included both the GISS land Temperature station (dTs) datasets - dTS 1200 km and dTs 250 km - which differ in how far temperature data may be extrapolated from a weather station.

And if you are measuring from 1980 then satellite data are the only reliable datasets because they just can not fiddle one year relative to another the way Giss / Had / CW do.

Better established datasets, like NOAA NCEP and NASA GISS, had already shown both 2005 and 2010 had broken the 1998 record.

GISS is in good agreement with all other modern datasets.

The fact that GISS shows more warming since 1990 than any other dataset indicates this possibility (and the others are not pure as driven snow here either).

Figure 2: The data (green) are the average of the NASA GISS, NOAA NCDC, and HadCRUT4 monthly global surface temperature anomaly datasets from January 1970 through November 2012, with linear trends for the short time periods Jan 1970 to Oct 1977, Apr 1977 to Dec 1986, Sep 1987 to Nov 1996, Jun 1997 to Dec 2002, and Nov 2002 to Nov 2012 (blue), and also showing the far more reliable linear trend for the full time period (red).

Unfortunately, the British researchers have been acting closely in league with their U.S. counterparts who compile the other terrestrial temperature dataset — the GISS / NCDC dataset.

Just imagine how much easier it would be to look at the relative differences between GISS / CRUTEM / BEST etc if these datasets where all stored in a fully relational structured common format.