Sentences with phrase «for giss»

Actually, Victor, I did a little sub analysis just on CRN1, just for GISS (because easier to access and display than NCEI).

Finally, Marco said «why don't you give a link to the grid with Barrow that you claim only shows a 0.3 anomaly for the 1981 - 2008 period vs 1951 - 1980 (standard for GISS)?

However, for the GISS temperature series the hypothesis of a random walk is clearly rejected if one allows for the presence of a trend.

And why don't you give a link to the grid with Barrow that you claim only shows a 0.3 anomaly for the 1981 - 2008 period vs 1951 - 1980 (standard for GISS)?

b) then start on the CO2 forcing / CO2 concentration time series analysis which would similar process as just done of the analysis for GISS time series

I chose 2 lags because I found I (1) with that for GISS.

FWIW: The values for the GISS data over this period alone are: 0.0062 -11.9902 0.0004 0.7691 0.660860704 0.163412896 243.5801133 125 6.504508474 3.337971841 According to EXCEL, and contrary to your intuition, the uncertainty in slope for the GISS data, measured in C / year, is larger than the uncertainty in the slope for the differences.

Using the Model E forcings through 2000 and projecting them through 2100 based on the IPCC atmospheric CO2 concentration estimates for these two scenarios gave the following empirical climate sensitivities for GISS OA:

I went to the CMIP archive to see if I could get the top - of - atmosphere (TOA) forcing for the GISS model month by month, but the GISS folks didn't archive that data.

This is just a brief note to point out that a few graphs that I have put together showing Ocean Heat Content changes in recent decades had an incorrect scaling for the GISS model data.

Figure S1: (a-g): Ensemble - average instantaneous radiative forcings and ocean heat uptake rates (thick lines) and individual ensemble members (thin) for GISS - E2 - R single - forcing experiments.

I have now uploaded GMST and top of atmosphere radiative imbalance data from 1850 on for the GISS - E2 - R runs used in Marvel et al..

I have now downloaded and processed CMIP5 data for the GISS - E2 - R single forcing runs, so I can show the spatial effects of LU forcing on simulated surface temperatures.

For the GISS analysis, normal always means the average over the 30 - year period 1951 - 1980 for that place and time of year.

The reality was that it actually gave the correct answer for GISS climate sensitivity over the temperature interval tested.

, and they only did it for GISS - E2 - R NINT, not the other five GISS - E2 model variants.

[2] The Historical simulations have an average temperature anomaly of 0.84 °C for 1996 — 2005 relative to 1850, whereas HadCRUT4v4 shows an increase of 0.73 °C from 1850 — 1859 to 1996 — 2005, and Figure 7 of Miller et al. 2014 shows consistently greater warming for GISS - E2 - R than per GISTEMP since 2000.

If one strips out the CO2 contributions, of 1.38 W / m2 for AR5 (based on an F2xCO2 of 3.71 W / m2) and of ~ 1.53 W / m2 for GISS - E2 - R (based on an ERF F2xCO2 of 4.1 W / m2) the the contribution of the other long lived GHG is 0.92 W / m2 per AR5 and ~ 1.86 W / m2 for GISS - E2 - R.

However, it is possible that a corrigendum was issued for the GISS - E2 - R results, and the data accessible via the CMIP5 portals updated.

Another option is to regress T on lagged F. I've found that using an exponential forcing decay with a time constant of ~ 2 years works well (gives the best fit) for GISS - E2 - R.

If that run is not considered an outlier it does not speak well for the confidence inspired for the GISS model as a predictor.

Remarkably, the Marvel et al. reworked observational estimates for TCR and ECS are, taking the averages for the three studies, substantially higher than the equivalent figures for the GISS - E2 - R model itself, despite the model exhibiting faster warming than the real climate system.

3) Can you confirm that the temperature and net flux data for GISS - E2 - R, available via the CMIP5 portals and KNMI Climate Explorer are based on a model corrected to fix the ocean heat transport problem which you identified in the Russell ocean model in your 2014 paper?

Marvel et al. state, in their Figure 1 legends, TCR and ECS estimates for GISS - E2 - R implied for ERF basis forcings by the ΔT and ΔF − ΔQ values.

FORTRAN 90 source and documentation for the GISS ModelE series of coupled atmosphere - ocean models.

(This is the basis for the GISS method).

Similarly for the GISS data, the trend since 1979 is 0.17 degrees C per decade (between 0.13 and 0.21 degrees C at 95 % confidence levels).

For GISS, UAH, and HadCRUT4, using the data available at woodfortrees because it was easy to get, the key year is 2007 (i.e., the 1979 - 2007 trend is larger than any previous trend).

I live here in Baltimore where it currently is a 6 degrees below normal anomaly gathered off a tar roof downtown at Customs House in U.S. Historical Climatology Network (USHCN) for GISS data, decades of city development too that area, police cars on street would raise reported temperature.

It is ironic that NASA puts up the satellites, but does not use them for GISS temperature series.

Look at the trend for the GISS - ER runs.

Girma also needs to do the calculation for GISS and NCDC otherwise it's just cherrypicking.

Springfield for GISS matches Springfield for BEST.

... and this was after showing that the trends for 2000 - 2009 for GISS, RSS and UAH were all positive — but Tamino excluded 2010 from the range as it hadn't begun yet.

And then he would do the same for the GISS web interface with regard to Springfield.

Furthermore, because of the small number of samples and the high «noise» of the samples around the trends, the effective AR - 1 corrected 1 sigma error is 0.94 C for GISS, 0.42 C for CRUT, and 0.61 C for NCDC.

Back in December of 2009, after Tamino showed postive warming trend for GISS, RSS and UAH from 1980 to 2000.

When you run the cross correlations over the complete period, you find that the R2 is 0.796 for the CRUT data, 0.761 for GISS, and 0.800 for NCDC.

The R2 values are 0.001 for the GISS data, 0.132 for CRUT, and 0.041 for NCDC, showing that the trends are also statistically invalid.

And since HadCrut shows the same sorts of variation GISS shows, you should believe the same for it as you believe for GISS in regard ti this issue.

I submitted my comment with the URL for the GISS data of Springfield when I saw you did the same thing.

He found it a much higher slope for GISS than Brandon had portrayed.

For each station, the anomaly is basically the difference, for each month say, between the current value and the mean value for some reference period (1951 - 1980 for GISS).

If so, it should be possible for GISS etc. to re-do their calculations with all the data.

I'm in the process of produing a similar thread but for the GISS raw / adjusted dataset (GISS is just as bad!).

I have not done the monthly lag correlations for RSS, but when I did it for the GISS data 1979 - 2007, the DW statistic on the regression residuals showed a very significant positive autocorrelation.

I calculated the Durbin Watson statistic (DW) for autocorrelation for the GISS time series 1979 - 2007 (using the residuals from the anomaly regression) for monthly data and determined a DW = 0.83 indicating a strong positive autocorrelation.

This still given a negative trend for HadCrut but a positive one for GISS, RSS and UAH.

For the GISS explanation read this until you understand it: http://www.skepticalscience.com/3-levels-of-cherry-picking-in-a-single-argument.html

You begin the post with a discussion of the GLOBAL anomalies for GISS, UAH, and RSS.