Jump to content

Arctic/Antarctic Sea Ice - what to make of it?


Recommended Posts

says you, the guy who hung his hat on "delayed solar"! :lol: Hey now, weren't you the guy who, just a few posts back, acknowledged that in relation to GHG increases, any presumptive estimate on solar impact is insignificant. Ride that insignificant wave of yours, hey!

Small != insignificant. But continue misrepresenting me if you want. Also you are confusing a claim about future climate change with a claim about what caused past climate change.

Link to comment
Share on other sites

  • Replies 301
  • Created
  • Last Reply

Top Posters In This Topic

I never claimed that 30 W/m^2 oscillations in solar insolation would bias estimates of temperature changes (which wouldn't make sense given you just have oscillations, which have an expected value of zero). My claim was that it makes the estimates and the results from CMIP5 models less reliable.

But if you purposely wish to confuse claims about something biasing an estimate with claims about an estimate being less reliable then that is your choice.

you truly are the King... of BS! You made a reference to "30 W/m^2 zonal oscillations" in 4 posts... in three of those 4 posts you simply dropped the exact words/phrase "30 W/m^2 zonal oscillations" without adding anything else. It was only in your initial post reference, where you linked the study, that you actually stated the following: "That's far more than the increase in solar insolation since the Mauder Minimum. Yet you expect me to believe that CMIP5 is supposed to be reliable in determining how much warming since 1950 was due to changes in solar irradiance? CMIP5 models are nonsense and conclusions made from CMIP5 are nonsense." Oh wait... you also did say the following: "Because I think it's a major nail in the coffin of the reliability of CMIP5 models."

and yet you won't acknowledge your bonehead play in applying yet another of your broadest of broad brushes to the entire complement of CMIP5 models... again:

"as I said, it takes an investment in time to flush out BS! You've made blatant, across-the-board, all encompassing reference to CMIP5 models and 30 W/m^2 zonal oscillations. Of course, as it turns out, this was found in only 8 of 30 models... of those 8, the worst case was found in only one obscure model, with some of those 8 @ 24... and others @ 3... depending on the time-step being used. But don't let that stand in the way of your blusterbus! Of course, none of those 8 models are the profiled, mostly used models... but why let that get in the way of your blusterbus! Equally, per the paper author's own statement, this has no affect on the global temperature estimates... did you actually read the paper you're blustering about? :lol: More pointedly, for those 8 models it had no effect on any trends. Most pointedly, the affect averaged out and only has consideration for regional impacts... do you know of any CMIP5 models being used for regional weather observations? I kid, I kid!

what was that you said Mr. BroadBrusher? ... sumthin bout it being "a major nail in the coffin"! :D

Link to comment
Share on other sites

Small != insignificant. But continue misrepresenting me if you want. Also you are confusing a claim about future climate change with a claim about what caused past climate change.

small => trivial; limited in importance or significance... you know, insignificant! :D I didn't confuse anything; I simply mocked your repeat references to "delayed solar" and all the blustering bravado you attached to it... and used that as a convenient measuring bar for the insignificant small TSI play you were similarly extending upon.

.

Link to comment
Share on other sites

The misrepresentation continues.

biasedness != reliability.

small != insignificant.

Keep pretending otherwise if you wish.

yes it does! By you... and I didn't say anything about your bias versus reliability? :lol: Your small whine is of limited significance... its insignificant!

Link to comment
Share on other sites

I'm pretty sure that the recent empirical data (even using Cowtan and Way) has gone well outside of the 95% confidence interval of the CMIP3 predictions that used data ending in 2000.

(per RealClimate: "a graph showing the annual mean anomalies from the CMIP3 models plotted against the surface temperature records from the HadCRUT4, NCDC and GISTEMP products (it really doesn’t matter which). Everything has been baselined to 1980-1999 (as in the 2007 IPCC report) and the envelope in grey encloses 95% of the model runs".)

model122.jpg

.

Link to comment
Share on other sites

Here's what happens when you include other RCPS:

WGI_AR5_Fig11-25.jpg

Yep. Outside of the 95% confidence interval.

you didn't identify the Figure... as in IPCC AR5 WG1 11.3.6.3 (Figure 11.25):

an update on that figure keyed to your 95% confidence interval reference... per Ed Hawkins:

fig-nearterm_all_UPDATE_2015a.png

... a comparison of CMIP5 simulations & observations of global mean surface air temperature, using a 1986-2005 reference period, and is an updated version of Figure 11.25 from IPCC AR5. The HadCRUT4.3 observations are shown in black with their 5-95% uncertainty. Several other observational datasets are shown in blue.

The grey shading shows the CMIP5 5-95% range for historical (pre-2005) & all future forcing pathways (RCPs, post-2005); the grey lines show the min-max range. The red hatching indicates the IPCC AR5 assessed likely (>66%) range for the 2016-2035 period. The UK Met Office forecast for 2015 is shown by the green error bar.

There are several possible explanations for why the observations are at the lower end of the CMIP5 range. First, there is internal climate variability, which can cause temperatures to temporarily rise faster or slower than expected. Second, the radiative forcings used after 2005 are from the RCPs, rather than as observed. Given that there have been some small volcanic eruptions and a dip in solar activity, this has likely caused some of the apparent discrepancy. Third, the real world may have a climate sensitivity towards the lower end of the CMIP5 range. Last, the exact position of the observations within the CMIP5 range depends slightly on the reference period chosen. A combination of some of these factors is likely responsible.

as you said, "Yep. Outside Inside of the 95% confidence interval".

Link to comment
Share on other sites

I stand corrected with respect to CMIP3 projections using the A1B scenario in 2000 being falsified by temperature trends.

For the second post, even with the update to HadCRUT 4.3, you can't say that observations aren't outside the 95% confidence interval. For example, the median temperature estimate for 2011 is clearly below the 95% confidence interval for 2011.

Anyway, while seeing if observations are inside the 95% confidence interval might be a simplistic way to check by eyeball if the data as validated the model predictions or not, it isn't the correct way to test the hypothesis. It is possible for the data to validate the model even if an observation lies outside the 95% confidence interval (for example, if you have 20 observations of annual temperature, then chances are at least 1 year will be outside of the 95% confidence interval) and it is also possible for the data to falsify the model even if no observations lie outside the 95% confidence interval (for example, if the majority lie at the bottom of the confidence interval).

So let's properly test the hypothesis of if the RCP projections are validated by observations. Now the 95% confidence intervals above are all annual confidence intervals. It would be nice to assume that observations in each year are independent, but that would be unfair since residuals around the temperature trend clearly have an autocorrelation. So I will need to first determine the autocorrelation of temperature residuals around their trend.

So let's take 1850-2005 for Cowtan and Way version 2.0: http://www-users.york.ac.uk/~kdc3/papers/coverage2013/had4_krig_annual_v2_0_0.txt. First I'll obtain of the residuals after removing a quadratic trend. Next I'll fit the AR(1) autoregressive model without constant to the residuals. I get an autocorrelation coefficient of 0.5846.

The next thing I need are the bounds of that 95% interval for each year. Unfortunately, I can't find it in a text or excel file or something, so I'll obtain it from that image you provided using a program known as GetData + Linear interpolation. I get:

Year: Lower Bound: Upper Bound:

2005 0.0233 0.5581

2006 0.0756 0.5291
2007 0.0953 0.5617
2008 0.1183 0.5693
2009 0.1298 0.5824
2010 0.1398 0.6129
2011 0.1710 0.6405
2012 0.1922 0.6760
2013 0.2086 0.7123
2014 0.2247 0.7524

Now, let's turn this into the median estimate and the standard error for sake of later calculations (assuming a roughly normal distribution here; the standard error corresponds to the length of the confidence interval divided by 3.92):

Year: Median: Standard Error:

2005 0.2907 0.1364
2006 0.3024 0.1157
2007 0.3285 0.1190
2008 0.3438 0.1151
2009 0.3561 0.1155
2010 0.3764 0.1207
2011 0.4058 0.1198
2012 0.4341 0.1234
2013 0.4605 0.1285
2014 0.4886 0.1346

Now the Cowtan and Way data needs to be converted to anomaly from 1986-2005 baseline to be comparable. This gives:

Year: Temperature Anomaly:

2005 0.2818
2006 0.2328
2007 0.2598
2008 0.1228
2009 0.2528
2010 0.3258
2011 0.1878
2012 0.2098
2013 0.2258
2014 0.3018

Now let's obtain the displacement of observation from the median by year. I'll also list the standard error after you include the observational uncertainty in global temperature according to Cowtan and Way:

Year: Displacement: SE for prediction: SE for observation: Total SE:

2005 -0.0089 0.1364 0.0300 0.1397
2006 -0.0695 0.1157 0.0290 0.1193
2007 -0.0687 0.1190 0.0290 0.1225
2008 -0.2210 0.1151 0.0300 0.1189
2009 -0.1033 0.1155 0.0300 0.1193
2010 -0.0506 0.1207 0.0310 0.1246
2011 -0.2180 0.1198 0.0310 0.1237
2012 -0.2243 0.1234 0.0310 0.1272
2013 -0.2347 0.1285 0.0310 0.1322
2014 -0.1868 0.1346 0.0310 0.1381

Now I can't just assume each displacement is independently distributed. There is clearly auto correlation. I.e. the displacement for 1 year should equal ~0.5846 times the displacement of the previous year plus a residual. So I want to look at the displacement for 1 year minus 0.5846 times the displacement of the previous year to see if the observations agree with model predictions or not. Now to be fair there is uncertainty associated with the autocorrelation constant 0.5846 (it has a standard error of 0.0662) and I also need to include the uncertainty of the observation of the previous year:

Year: Displacement - 0.5846*Previous_Displacement: SE for first displacement: SE due to second observation: SE due to autocorrelation constant: Total SE:

2006 -0.0643 0.1193 0.0229 0.0062 0.1216
2007 -0.0280 0.1225 0.0222 0.0175 0.1257
2008 -0.1808 0.1189 0.0222 0.0174 0.1222
2009 0.0259 0.1193 0.0229 0.0311 0.1254
2010 0.0098 0.1246 0.0229 0.0213 0.1285
2011 -0.1884 0.1237 0.0237 0.0149 0.1269
2012 -0.0969 0.1272 0.0237 0.0309 0.1331
2013 -0.1035 0.1322 0.0237 0.0314 0.1379
2014 -0.0496 0.1381 0.0237 0.0321 0.1438

Okay, now let's normalize everything to Displacement - 0.5846*Previous_Displacement divided by its standard error:

2006 -0.52906
2007 -0.22309
2008 -1.47946
2009 0.20645
2010 0.07658
2011 -1.48496
2012 -0.72813
2013 -0.75069
2014 -0.34482

Now if the the predictions agree with observations, then the mean of the above values should be zero. The mean is -0.58413. We can test if predictions agree with observations using a simple Z-test. Given that there are 9 observations, under the hypothesis that predictions agree with observations, the probability distribution of the mean should have a mean of zero and a standard deviation of 1/sqrt(9). The z-statistic is thus -1.7524. The p-value of this z-statistic is 0.0797.

So I cannot reject the hypothesis that predictions agree with observations at the 5% significance level, though it's pretty close.

Edited by -1=e^ipi
Link to comment
Share on other sites

I'll also point out that Emissions over 2006-2014 were higher than all of the RCPs except 8.5:

nclimate2148-f1.jpg

So only RCP 8.5 can be representative of what happened over the past 9 years, where as if CMIP5 was making non-biased predictions then the results from running the other 3 RCPs is expected to be lower than observations. Figure 11.25 includes all RCPs, thus the confidence interval is larger and lower than it should be based on emission rates from 2005-2014.

If you account for the fact that only RCP 8.5 accurately reflects what happened since 2006, then that p-value of 7.97% will decrease and should easily go below the 5% significance level.

Thus CMIP5 predictions probably do not agree with empirical observations (I need the RCP 8.5 confidence interval to properly test).

Edited by -1=e^ipi
Link to comment
Share on other sites

Okay, using GetData on this:

nclimate1716-f1.jpg

I get that the warming from 2005-2014 under RCP 8.5 is approximately 0.00558 C larger than under the mean of the RCPs. If I assume that the difference in median temperature prediction between RCP 8.5 and the mean of the RCPs is roughly proportional to time for 2005-2014, then I should adjust my displacement values to take this into account. I get (I'll assume that the standard error does not change by restricting things to RCP 8.5; even though this restriction should reduce the standard error; I'll give the benefit of the doubt to the claim that model predictions agree with observations):

Year: Displacement: SE for prediction: SE for observation: Total SE:

2005 -0.0089 0.1364 0.0300 0.1397
2006 -0.0702 0.1157 0.0290 0.1193
2007 -0.0699 0.1190 0.0290 0.1225
2008 -0.2229 0.1151 0.0300 0.1189
2009 -0.1058 0.1155 0.0300 0.1193
2010 -0.0536 0.1207 0.0310 0.1246
2011 -0.2217 0.1198 0.0310 0.1237
2012 -0.2286 0.1234 0.0310 0.1272
2013 -0.2396 0.1285 0.0310 0.1322
2014 -0.1923 0.1346 0.0310 0.1381

Year: Displacement - 0.5846*Previous_Displacement: SE for first displacement: SE due to second observation: SE due to autocorrelation constant: Total SE:

2006 -0.0650 0.1193 0.0229 0.0062 0.1216
2007 -0.0289 0.1225 0.0222 0.0175 0.1257
2008 -0.1820 0.1189 0.0222 0.0175 0.1223
2009 0.0245 0.1193 0.0229 0.0313 0.1255
2010 0.0082 0.1246 0.0229 0.0215 0.1285
2011 -0.1903 0.1237 0.0237 0.0153 0.1269
2012 -0.0991 0.1272 0.0237 0.0312 0.1331
2013 -0.1059 0.1322 0.0237 0.0317 0.1380
2014 -0.0523 0.1381 0.0237 0.0324 0.1438

Normalized:

2006 -0.53416
2007 -0.23005
2008 -1.48847
2009 0.19530
2010 0.06372
2011 -1.49935
2012 -0.74405
2013 -0.76786
2014 -0.36327

The mean is now -0.59646 and the z-statistic becomes -1.7894. The p-value is now 0.0736. Lower, but not enough to make the difference statistically significant.

I can't reject the hypothesis that model predictions agree with empirical data.

Link to comment
Share on other sites

I'm pretty sure that the recent empirical data (even using Cowtan and Way) has gone well outside of the 95% confidence interval of the CMIP3 predictions that used data ending in 2000.

.

I stand corrected with respect to CMIP3 projections using the A1B scenario in 2000 being falsified by temperature trends.

another retraction from you... bully!

you introduced this "model predictions versus observations falsified to the 95% confidence level" theme... throughout that fixation of yours you've offered nothing to speak to possible reasons why model predictions might be "running higher". Your whole premise is to simply play the "models are useless" card... imagine that... coming from you! Go figure.

of course, you skirted right over the statement reference I provided that actually spoke to this; clearly, not something you're at all interested in as it clearly gets in the way of your agenda, hey! Again, possible explanations for why observations are at the lower end of the CMIP5 range:

- First, there is internal climate variability, which can cause temperatures to temporarily rise faster or slower than expected.

- Second, the radiative forcings used after 2005 are from the RCPs, rather than as observed. Given that there have been some small volcanic eruptions and a dip in solar activity, this has likely caused some of the apparent discrepancy.

- Third, the real world may have a climate sensitivity towards the lower end of the CMIP5 range.

- Last, the exact position of the observations within the CMIP5 range depends slightly on the reference period chosen.

Link to comment
Share on other sites

Anyway, while seeing if observations are inside the 95% confidence interval might be a simplistic way to check by eyeball if the data as validated the model predictions or not, it isn't the correct way to test the hypothesis.

which didn't stop you from doing your own eyeballing and offering your own summary assessment... in this post as well as many times previously. Oh wait, of course, this was your opening to spew forth presumptive methods and resulting data... that even if someone was inclined they couldn't absolutely check your process/results... notwithstanding you continue to reach well and beyond the audience of this board. To what end? That blog reference I provided you is the forum to take your low(er)-level challenge to.

oh wait... is this you not challenging further?

So I cannot reject the hypothesis that predictions agree with observations at the 5% significance level, though it's pretty close.

in any case, from that same referenced expert I previously provided... and from his blog that I previously linked to, speaking directly to the C/W you beat upon:

UPDATED_11-25.png

The figure below above is an updated version of Figure 11.25 from IPCC AR5 which includes the Cowtan & Way data (CW13, blue) and HadCRUT4 data (black, provisional for 2013). The CW13 data shows an increased global temperature trend, and is within the 5-95% uncertainty of the HadCRUT4 observations (red). CW13 is also just within the 5-95% CMIP5 range (light grey).

.

Link to comment
Share on other sites

Okay, using GetData on this:

I can't reject the hypothesis that model predictions agree with empirical data.

you can't reject; oh my! But hey now, you didn't provide your source... damn, that gets me points and a suspension warning... perhaps I'm special to the moderator! Here, let me help ya out: Robustness and uncertainties in the new CMIP5 climate model projections (Knutti & Sedláček) Of course, both those authors are well recognized, particularly Knutti who is often a go-to source for mainstream media.

is applying GetData against that scale of graph really a prudent undertaking? In any case, the thrust of that single study (versus the overall synthesis of studies that the IPCC graphics typically represent), reflects upon just how much AR5/CMIP5 has progressed (or not) over AR4/CMIP3, along with focused uncertainties within the CMIP5 ensemble. The study abstract includes an interesting summary statement... surely this wouldn't be why you didn't provide the source:

Interestingly, the local model spread has not changed much despite substantial model development and a massive increase in computational capacity. Part of this model spread is irreducible owing to internal variability in the climate system, yet there is also uncertainty from model differences that can potentially be eliminated. We argue that defining progress in climate modelling in terms of narrowing uncertainties is too limited. Models improve, representing more processes in greater detail. This implies greater confidence in their projections, but convergence may remain slow. The uncertainties should not stop decisions being made.

since your go-to here was the scientist Knutti, what's that about looking a gift-horse in the mouth: Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes:

We show that at the present-day warming of 0.85 °C about 18% of the moderate daily precipitation extremes over land are attributable to the observed temperature increase since pre-industrial times, which in turn primarily results from human influence. For 2 °C of warming the fraction of precipitation extremes attributable to human influence rises to about 40%. Likewise, today about 75% of the moderate daily hot extremes over land are attributable to warming. It is the most rare and extreme events for which the largest fraction is anthropogenic, and that contribution increases nonlinearly with further warming. The approach introduced here is robust owing to its global perspective, less sensitive to model biases than alternative methods and informative for mitigation policy, and thereby complementary to single-event attribution. Combined with information on vulnerability and exposure, it serves as a scientific basis for assessment of global risk from extreme weather, the discussion of mitigation targets, and liability considerations..

NYT- New Study Links Weather Extremes to Global Warming

The study by Dr. Fischer and his colleague Reto Knutti, of the Swiss Federal Institute of Technology in Zurich, is not the first to attribute large-scale changes in extreme weather to human influence on the climate. But it is among the first to forecast, on a global scale, how those extremes might change with continued global warming.

.

Link to comment
Share on other sites

I'll also point out that Emissions over 2006-2014 were higher than all of the RCPs except 8.5:

nclimate2148-f1.jpg

So only RCP 8.5 can be representative of what happened over the past 9 years, where as if CMIP5 was making non-biased predictions then the results from running the other 3 RCPs is expected to be lower than observations. Figure 11.25 includes all RCPs, thus the confidence interval is larger and lower than it should be based on emission rates from 2005-2014.

If you account for the fact that only RCP 8.5 accurately reflects what happened since 2006, then that p-value of 7.97% will decrease and should easily go below the 5% significance level.

Thus CMIP5 predictions probably do not agree with empirical observations (I need the RCP 8.5 confidence interval to properly test).

oh my! Yet another source you don't provide! Of course, this is one you clearly don't want to broach discussion around, hey! You know, that whole "policy narrative for a dangerously warming world" thingee! Fake-skeptics like you rarely want to go there, right? Should I provide your missing source, or not? :lol:

you say, "only RCP 8.5 can be representative"... you mean the effective "BAU/no mitigation scenario"? I guess that about speaks to where we're at in this lead-up to the upcoming Paris COP, right? Interestingly, within that graph you provide, you don't speak to the 5°C temperature projection on that 'increasing emissions' scenario path, do you? Then again, aren't you one of those Adapt-R-Us only guys? That's you right?

of course, this really highlights you've taken few steps to actually speak to what the emission scenarios are even about... rates, magnitudes, story lines, socio-economic development, etc.; details, smetails!

Link to comment
Share on other sites

Please define what you mean by 'beat upon' and explain how I 'beat upon' Cowtan and Way.

But hey now, you didn't provide your source.

Source? The source is the calculations I did in this thread. I explained the methodology, data sets, etc. See my above posts.

you say, "only RCP 8.5 can be representative"... you mean the effective "BAU/no mitigation scenario"?

RCP 8.5 is the most representative from 2006-2014. That doesn't mean RCP 8.5 is representative of BAU; RCP 8.5 makes assumption after assumption in favour of extreme warming. It isn't a representative BAU scenario, it is an alarmist scenario. Fitting an exponential trend to CO2 emissions per capita and a logistic trend to population demonstrates strong divergence between RCP 8.5 and what you would expect under BAU.

you don't speak to the 5°C temperature projection on that 'increasing emissions' scenario path, do you?

The temperature projections are based upon CMIP5, and I was testing the hypothesis of if CMIP5 predictions are validated by the instrumental data. I already explained a number of reasons why I think that CMIP5 models are oversensitive and thus are overestimating future warming.

Link to comment
Share on other sites

The z-statistic becomes -1.7894. The p-value is now 0.0736.

In order to get a p-value of 0.5, the z-statistic needs to reach 1.96 or -1.96. So if the trend continues, the the p-value will reach 0.05 in approximately (1.96/(1.7894/sqrt(9)))^2 = 10.8 years.

So CMIP5 predictions might be falsified in 2 years time.

Link to comment
Share on other sites

Fitting an exponential trend to CO2 emissions per capita and a logistic trend to population demonstrates strong divergence between RCP 8.5 and what you would expect under BAU.

I explained my methodology here: http://www.mapleleafweb.com/forums/topic/24202-what-is-the-correct-value-of-climate-sensitivity/?p=1048951

And I made improvements to my methodology here: http://www.mapleleafweb.com/forums/topic/24202-what-is-the-correct-value-of-climate-sensitivity/?p=1049590

Link to comment
Share on other sites

I'll also point out that I did the two sided statistical test rather than the one sided statistical test (in which case the p-value would be cut in half). So I'm 96.32% certain that CMIP5 models are overpredicted temperature for 2006-2014, but only 92.64% certain that the observations are inconsistent with the CMIP5 predictions.

Link to comment
Share on other sites

Btw, using the updated methodology, I get a median estimate of 14.64 GtC per year by 2100 under a no-mitigation scenario.

For CH4 and N2O emissions, the divergence between RCP 8.5 is even more ridiculous. CH4 and N2O emissions per capita have been rapidly decreasing globally since the 70s, yet magically they are going to start increasing under the RCPs. With the same model I get median estimates of 167 Tg CH4 per year in 2100 and 5.35 Tg N per year in 2100.

In RCP 8.5 we get:

emissions-graph-rpc.PNG

Link to comment
Share on other sites

:lol: hey buddy... you talkin' to yourself much!

but hey now, didn't you know... quoting yourself is verboten here! That gets me penalty points and suspension warnings! Geezaz, I must be real special for the prowling moderator to keep singling me out.
.


Source? The source is the calculations I did in this thread. I explained the methodology, data sets, etc. See my above posts.


no - you did not provide the source of your graphic! You absolutely know that's what I'm referring to since I actually provide the source... your missing source... for you! Duh. I didn't "see" you do any calculations in the thread... but like I said, what's your point in laying down reams of data and described methodology. Again, even if someone here had the inclination to actually check your bluster-bus routine, which no one really does (even if they had the capabilities), what's your point? Ever wonder why no one, as I'm aware (granted I had a recent 6-month self-imposed hiatus), has ever engaged you in your "low-level wizardry"? There's a ton of blogs that you could target, where you'd get real feedback to the same level you're presenting here... it's freakin' amazing that you've chosen this obscure political focused discussion board to play out your fantasies and agenda. Just who do you think is the audience you might be reaching here? :lol: For all your nonsense, you lose most everyone's interest with your low(er)-level prattle... if only you actually knew how to articulate your fake-skepticism to a level that most here could understand, appreciate... and engage you in! What a concept.
.

Link to comment
Share on other sites

RCP 8.5 is the most representative from 2006-2014. That doesn't mean RCP 8.5 is representative of BAU; RCP 8.5 makes assumption after assumption in favour of extreme warming. It isn't a representative BAU scenario, it is an alarmist scenario.

"an alarmist scenario"? Say what? In the following quote of yours, is this you stating that the, as you say, "representative and accurate reflection" of RCP 8.5 is representative and an accurate reflection of, wait for it... an alarming scenario being played out across the world? :lol: Cause like the RCP 8.5's increasing GHG emissions over time (over that time), certainly are leading to higher GHG atmospheric concentrations. Since the other RCP scenarios are either drastic mitigation and/or stabilization focused, it sure seems that RCP 8.5 is today's "business as usual"... wouldn't you say, hey!

.

So only RCP 8.5 can be representative of what happened over the past 9 years...

If you account for the fact that only RCP 8.5 accurately reflects what happened since 2006...

.

Link to comment
Share on other sites

no - you did not provide the source of your graphic!

You know you can always click on the image and it tells you were it's from, right? On the last page, I provided images from nature, skepticalscience and the ipcc.

Edited by -1=e^ipi
Link to comment
Share on other sites

Since the other RCP scenarios are either drastic mitigation and/or stabilization focused, it sure seems that RCP 8.5 is today's "business as usual"... wouldn't you say, hey!

I explained why based on empirical data RCP 8.5 cannot be considered representative of a business as usual scenario. It's interesting that in AR4 A1B was arguably the BAU representative BAU scenario, yet come AR5 8.5 is suddenly the BAU scenario. What a change:

nclimate1716-f1.jpg

Link to comment
Share on other sites

You know you can always click on the image and it tells you were it's from, right? On the last page, I provided images from nature, skepticalscience and the ipcc.

what a lame-azzed weaselly reply! You know that a graphic link doesn't necessarily allow you to directly find what article/paper/study reference it associates with. In your latest forays it's quite clear why you didn't want to draw attention to the actual references, in themselves, that your graphics were pulled from. You know, cause neither of them fit your fake-skeptic agenda.

.

Link to comment
Share on other sites

I explained why based on empirical data RCP 8.5 cannot be considered representative of a business as usual scenario. It's interesting that in AR4 A1B was arguably the BAU representative BAU scenario, yet come AR5 8.5 is suddenly the BAU scenario. What a change:

and yet you acknowledged that RCP 8.5 is the only AR5 scenario that represents... and accurately reflects... the last ~15 years of increasing GHG emissions and increasing atmospheric concentrations - you know, business as usual for 'BigOil'. Your attempt to directly compare SRES to RCP is weak... and the actual paper (that you refuse to link to) explains why. A few posts back you dropped yet another of your unsubstantiated statements/opinions when you claimed RCP 8.5, "makes assumption after assumption in favour of extreme warming..... it is an alarmist scenario".

Link to comment
Share on other sites

Join the conversation

You can post now and register later. If you have an account, sign in now to post with your account.
Note: Your post will require moderator approval before it will be visible.

Guest
Unfortunately, your content contains terms that we do not allow. Please edit your content to remove the highlighted words below.
Reply to this topic...

×   Pasted as rich text.   Paste as plain text instead

  Only 75 emoji are allowed.

×   Your link has been automatically embedded.   Display as a link instead

×   Your previous content has been restored.   Clear editor

×   You cannot paste images directly. Upload or insert images from URL.


  • Tell a friend

    Love Repolitics.com - Political Discussion Forums? Tell a friend!
  • Member Statistics

    • Total Members
      10,713
    • Most Online
      1,403

    Newest Member
    nyralucas
    Joined
  • Recent Achievements

    • Jeary earned a badge
      One Month Later
    • Venandi went up a rank
      Apprentice
    • Gaétan earned a badge
      Very Popular
    • Dictatords earned a badge
      First Post
    • babetteteets earned a badge
      One Year In
  • Recently Browsing

    • No registered users viewing this page.
×
×
  • Create New...