Keith Briffa and the Renaissance

While I was busy with the Antarctica analysis a blogfuss started at CA on Briffa's 1999 (!) Science presentation of various paleo reconstructions, particularly relating to whether a particular graph should have started in 1400 or 1550. A file had been discovered which showed data down to 1400, and if you plot it, it goes into oscillations in the years before 1550. Since it is clear that this is in a period of rapidly diminishing data, and very likely caused by that, I thought that would die fairly quickly, but no, as these things go, it was promoted to a grand ethical violation, megaphoned at WUWT, and taken up at the Air Vent, where it was seen as "unbelievable fraud".

The evidence is supposed to be this graph, taken from this paper: SEEING THE WOOD FROM THE TREES. KR Briff and TJ Osborn, Science, 1999

The pink bits have been helpfully added by Steve McIntyre, based on numbers he found in an XL file which had been apparently inadvertently attached to another paper's data archived at NOAA. It's undocumented there.

Well, it seemed clear to me that the available data is just getting low as we go back beyond 1550, and the wild swings are just the result of the growing noise, as you'd expect. And I haven't found anyone who seems to seriously think they reflect any kind of reality. So Briffa sensibly stopped at 1550 to avoid misleading the public. But no, it's apparently academic misconduct, another Yamal even. [ed.. But, but, wasn't that about actually proceeding with plots where data was running low? O, never mind, ethical violation etc].

So I belatedly saddled up and sallied forth to dispute at CA and tAV. I don't want to rehash that here, nor transfer the debate, which I'll continue there. In the course of it, there was argument about how much the data was actually reducing, and Steve McI pointed me to the archived list of sites at UEA. There isn't one for the Science paper but there is a list for the Nature paper that it references:

Briffa KR et al. (1998) Influence of volcanic eruptions on Northern Hemisphere summer temperature over the past 600 years. Nature 393, 450-455.

and a list for a subsequent paper

Briffa KR et al. (2001) Low-frequency temperature variations from a northern tree-ring-density network. J. Geophysical Research 106, 2929-2941.

From these, I could extract a graph for the rate at which the number of sites was reducing in the relevant period. I also made lists of the actual sites concerned.



The reason for plotting the JGR paper as well is that Steve M pushed the claim that the JGR paper had plotted down to 1400, while the Science paper stopped at 1550. They referenced different data sets, so I wanted to check if they perhaps had more data in the later paper. As you'll see, the answer is no, but they did claim in the second paper to have an improved method, which might well allow them to go further back in time.

Anyway, here is the plot of number of sites vs time, over all time.

As you can see, the number of sites is dropping rapidly before 1600, and is down to about 40 near 1550. Here is the expanded region between 1400 and 1600:

As you can see, the rate of decrease is quite sharp near 1550. There's no absolute rule on where you have to say that a plot has to be stopped. The noise rises relative to the signal in a continuous way, and I don't curently know how to quantify whether 40 sites is likely to be sufficient. But neither do the critics. What is clear is that the observed rapid changes observed in McIntyre's graph are closely associated with the steep reduction in data. In those circumstances, I would be very uncomfortable about presenting them as real. And I don't think referees would let me.

Here is a list of the first 61 sites from the 1998 Nature paper. I won't list the corresponding JGR sites, as it is very similar.

"ID"	"Name"	"Abbr"	"Genus"	"Lon"	"Lat"	"Start"	"End"
"276"	" 862A "	" Polar-Ural (historisch) "	" POU_LA "	" LASI "	65.63	66.87	914	1990
"253"	" 928A "	" Mangazeja (hist. + rez.) "	"MANGAZPC "	" PCOB "	82.3	66.68	1246	1969
"252"	" 928D "	" Mangazeja (historisch) "	"MANGAZLA "	" LASI "	82.3	66.68	1307	1698
"301"	" 913A "	" Zhaschiviersk "	"ZHASHIST "	" LASI "	142.62	67.45	1311	1708
"351"	" 1041A "	" Majakit river/village "	"MAJAKILA "	" LADA "	151.95	61.28	1338	1994
"278"	" 930A "	" Seimchan-river "	"SEIMCHLA "	" LADA "	151.72	63.52	1362	1991
"215"	" 745A "	" Sylvan Pass bei Cody "	" SYLVAN "	" PCEN "	-110.13	44.37	1388	1983
"264"	" 1017A "	" Nuleger river "	"NULEGELA "	" LADA "	127.43	71.22	1391	1990
"174"	" 691A "	" Jasper "	" SUNW "	" PCEN "	-117	52.25	1400	1983
"383"	" 9999Z "	" Tornetrask "	" TORNXX "	" PISY "	19.5	68.3	1400	1980
"206"	" 731A "	" Medicine Bow Peak "	" MEDI "	" PCEN "	-107.7	41.3	1401	1983
"372"	" 992A "	" Qamdo "	" QAMDOFI "	" PCBA "	96.95	31.08	1406	1994
"337"	" 1014A "	" Bilibina,Mali Anuj river "	"BILIBILA "	" LADA "	167.67	67.47	1432	1991
"382"	" 1060A "	"Tschokurdach,Ochotingna r "	"TSCHOKLA "	" LADA "	148.05	70.28	1434	1990
"48"	" 630A "	" Sierra da Crispo "	" CRISPO "	" PILE "	16.23	39.9	1441	1980
"332"	" 1032A "	" Andryuschkine "	"ANDRYULA "	" LADA "	145.77	69.28	1449	1991
"268"	" 899A "	" Olenjok "	"OLENJOLA "	" LAGM "	112.53	68.52	1450	1990
"214"	" 686A "	" Snow Bowl, San.Fr. Peak "	" SNOW "	" PCEN "	-110.2	35.43	1453	1983
"377"	" 1063B "	" Adycha river (flach) "	"ADYBOALA "	" LADA "	137.25	65.8	1481	1991
"304"	" 865A "	" Olenok-River "	"ZWANZGLA "	" LAGM "	119.1	68.52	1482	1990
"316"	" 540A "	" Lofoten, Loedingen "	" LOFOTN "	" PISY "	16.03	68.48	1485	1978
"333"	" 955A "	" Balshoia Anui "	"BALANULA "	" LADA "	165.42	66.22	1492	1991
"198"	" 692A "	" Highland Fire Outlook "	" HIGHL "	" PCEN "	-112.53	45.75	1496	1983
"209"	" 733A "	" Powder River Pass "	" POWDER "	" PCEN "	-107.05	44.15	1496	1983
"352"	" 981A "	" Omoloya river "	"OMOLOYLA "	" LADA "	132.98	70.95	1496	1991
"380"	" 1070A "	" Balygichan-river "	"BALYRILA "	" LADA "	154.58	62.13	1497	1994
"188"	" 762A "	" Barlow Pass, am Mt.Hood "	" BARLOW "	" PSME "	-121.65	45.32	1504	1983
"200"	" 746A "	" Granite Pass, Hunt Mtn. "	" HUNT "	" PCEN "	-107.87	44.78	1508	1983
"101"	" 689A "	"Hidden Peak, Wasatch Mtn. "	" ALTA "	" PCEN "	-111.63	40.57	1511	1983
"184"	" 702A "	" Yosemite Park, E Eingang "	" YOSE "	" PICO "	-119.25	37.8	1513	1983
"87"	" 629A "	" Col de Sorba, Mt.Renoso "	" SORBA "	" PINI "	9.2	42.07	1518	1980
"202"	" 760A "	" Lassen National-Park "	" LASSEN "	" TSME "	-121.52	40.45	1525	1983
"196"	" 723A "	" Galena Pass, Sawtooth NF "	" GALENA "	" PCEN "	-114.72	43.87	1530	1983
"208"	" 756A "	" Pike Peaks "	" PIKES "	" PCEN "	-105.03	39.33	1530	1983
"341"	" 974A "	" Cherskij "	"CHERSKLA "	" LADA "	163.05	68.8	1538	1991
"220"	" 961A "	" Ayakli river "	"AYAKLILA "	" LAGM "	97.53	69.53	1540	1990
"192"	" 755A "	" Electric Lake "	" ELEC "	" PCEN "	-111.33	39.58	1542	1983
"345"	" 1047A "	" Julietta north "	"JULINOLA "	" LADA "	153.97	61.17	1547	1994
"204"	" 732A "	" Lone Lake "	" LONE "	" PICO "	-118.47	37.17	1548	1983
"336"	" 973A "	" Batagay,Chandon-river "	"BATAGALA "	" LADA "	138.17	70.25	1550	1991
"221"	" 924A "	" Ayandina-River "	"AYANDYLA "	" LADA "	143.17	68.42	1553	1991
"125"	" 704A "	" Denali National-Park "	" DENALI "	" PCGL "	-149.58	63.67	1554	1983
"210"	" 700A "	" Sierra Blanca, (Ruidoso) "	" RUID "	" PSME "	-105.67	33.33	1554	1983
"339"	" 956A "	" Bulun river "	"BULUNLAD "	" LADA "	154.93	65.1	1554	1991
"353"	" 958A "	" Rossocha river "	"ROSSOCLA "	" LADA "	149.43	65.15	1555	1991
"187"	" 726A "	" Baldy Peak "	" BALDY "	" PSME "	-109.55	33.97	1556	1983
"348"	" 969A "	" Srednie-Kolymsk "	"KOLYMSLA "	" LADA "	153.7	67.25	1556	1991
"381"	" 1061A "	" Kubaka right bank middle "	"KUBA2LAG "	" LAGM "	159.95	63.67	1560	1994
"240"	" 864A "	" Kotuykan-River "	"KOTUYALA "	" LAGM "	104.25	70.58	1563	1990
"191"	" 748A "	" Crater Lake, NE-Medford "	" CRATER "	" TSME "	-122.17	42.97	1564	1983
"246"	" 926A "	" Kuonamka-River (trocken) "	"KUONLATR "	" LAGM "	112.82	69.93	1564	1990
"302"	" 906A "	" Zhigansk "	"ZHIGANPI "	" PISY "	122.33	66.52	1564	1991
"354"	" 970A "	" Sartan river "	"SARTANLA "	" LADA "	132.93	64.93	1564	1991
"190"	" 683A "	" Cottonwood Pass "	" COTTON "	" PCEN "	-107.58	38.67	1565	1982
"356"	" 980A "	" Tirekhtjakh river "	"TIREKHLA "	" LADA "	137.47	67.62	1565	1991
"203"	" 695A "	" Mt. Lemon "	" LEMON "	" PSME "	-110.78	32.45	1568	1983
"234"	" 927A "	" Khandiga-River "	"KHANDILA "	" LADA "	137.75	62.47	1568	1990
"238"	" 914A "	" Khotugn-Uladan-Tukulan "	"KHOTUGPI "	" PISY "	125.8	63.38	1568	1991
"228"	" 1011A "	" Balschaya Kamenka river "	"KAMENKLA "	" LAGM "	93.97	71.32	1569	1990
"243"	" 871A "	" Kulyumbe River "	"KULYUMLA "	" LASI "	89.07	68.05	1574	1990

Briffa et al Nature 1998: ftp://ftp.ncdc.noaa.gov/pub/data/paleo/treering/reconstructions/n_hem_temp/nhemtemp_data.txt

Trends in Antarctica

In a comment, Eric Steig suggested direct estimation of temperature trends in West Antarctica(WA) (as opposed to estination via fitted EOFs). For trend estimation, the methods of S09 and O10 have good and bad points. A plus is that they do estimate for a prescribed area. Against that is that it isn't obvious, with variable sea ice, what area to prescribe. A minus is the indirectness. Temperatures are used to fit basis functions (EOFs), and the trend is obtained from the fitted function. Whether through a small number of EOFs, or via regularisation (kgnd), this interpolated step restricts degrees of freedom. TempLS is designed for the direct estimation of trends via least squares model fitting, so I thought I would try that direct approach. I deferred the project while I looked for a suitable area weighting scheme, and that is now available.

Background to this post can be found in earlier discussions here, here, here, here, here, and here The discussion is prompted by two papers: S09, a 2009 Nature paper by Steig et al and RO10 (or O10), a 2010 J Climate paper by O'Donnell et al. There has been heated blog controversy about these papers - this post is just one entry point. Here is a Real Climate post following S09's original appearance, and here is Eric Steig commenting on O10 (this led to other heated postings). There are earlier discussions of area weighting by RO10 authors at tAV, eg here and here.

The results below broadly confirm the earlier spatial analyses, and follow the regional breakdown of S09 and O10, though often with higher trends. The continent ground station trend, 1958 to 2009 and not area weighted, was 0.175 ± 0.03 °C/decade. This is consistent with my earlier EOF-based result, and higher than S09 or O10. Area weighting brought it down to 0.114 ± 0.035 °C/decade, similar to S09 (with AVNRR). Adding equally weighted AVHRR pushed it up to 0.137 ± 0.037 °C/decade. I think these results add a degree of confirmation to those earlier analyses, and to my EOF-based analysis, and tend to confirm a positive warming trend, especially outside East Antarctica.

Something that needed attention is the regional tagging of the data I am using, which comes from the data Ryan put on line, and earlier from S09. There are four regions, Peninsula, WA, Ross Sea, EA. But the satellite tags are bounded inconsistently with the ground stations, so I retagged them as shown in this map. Ground stations are shown with large dots, and the subset of AVHRR stations (described here) are small dots, both colored by region.



That done, I have computed trends for these regions and the continent using the OLS methods of TempLS V1. As with some earlier posts, I have mixed AVHRR and ground stations, with various relative weightings (including ground only).

Continent ground station trends

These are difficulties with the sparse samples in some tof the regions, most notably with West Antarctica. So the whole continent is simplest. I'll start with unweighted (by area):

Stations reporting in each year	A map of ground stations colored by region

The trend is quite high at 0.175°C/decade. Now I'll add weighting by area, as described here. The map shows for a specific month, Dec 1987,  the triangular mesh created, the subdivision used to weight each station, and the circles show, by area, the size of the weights. I have not smoothed, nor tried to cut out the Weddell Sea.




The trend is lower, at 0.114°C/decade. This is in line with earlier observations that area weighting reduces the trend, probably by boosting the effect of the less rapidly warming East Antarctica stations, which occupy the largest region.

Adding AVHRR readings

As with previous posts, I've introduced the satellite AVHRR data, which comes as grid data, as artificial stations. I've selected only 1 in 4, so there are 1392 of them. A factor is needed to determine the relative weighting for combining them with ground stations. I now use a range from 0 (all satellite) to 1 (all ground), with 0.5 indicating that total weights for satellite and ground are equal. That is for all years, so that before 1980, there is ground data only, and the factor doesn't matter. Because of the way it is totalled, this means sat data gets a higher weighting (at 0.5) after 1980. This is a small effect.

So here are results from satellite data only. Like O10, I'm using Eric Steig's archived AVHRR data, which goes up to 2006 only.

As expected, the trend is quite high. One of the reasons for interest in S09 was that it looked into the discrepancy between the ground trend (lowish) and AVHRR (high).

So now looking at the combination of satellite and ground, remembering that before 1980 it is ground only:

So the trend is lower - closer to ground than AVHRR, reflecting the shorter period of AVHRR.

Antarctic Peninsula trends

So now I look at regional trends. I'll start with the Peninsula, which does not have problems of years without data. Here are ground stations only:

AS expected, the trend is high. Now if the AVHRR stations are included, with equal (total) weighting:

East Antarctica trends

Again firstly the ground stations:


As reflected in work in previous posts (and S09, O10) ground stations in East Antarctica have been warming very slowly. The inclusion of AVHRR results at nominally equal weighting increases this by a fairly small amount:

West Antarctica and Ross Sea

I found a specific difficulty with WA, which I think Ryan and Eric have mentioned. In about 1980, about the time AVHRR satellite information is available, there was also a change to measurement (AWS). Only one station in WA was reporting before then, Byrd, and there was a gap of four years with no readings at all. Byrd changed to AWS, and this is listed as a new station. So there is no way of bridging the gap without making some assumption about how the old Byrd relates to Byrd AWS. And even that would be slender information.

A first option for WA is to combine it with the Ross Sea stations, where there is a fuzzy boundary anyway. There are just enough stations pre-1980 to get a result:

Again, as expected, the trend is high. Adding AVHRR stations does not change this.

West Antarctica trends

The other option for WA is to look at results after 1980 only. That gives:

The trend is very high indeed, but so is the uncertainty, and the stations are few.

It seems the AVHRR data stabilizes the average when it is available, so just checking those years:


This did reduce the trend slightly, and improve the error range.

Area weighting and a 60 stations global temperature.

I've been experimenting more with area-based weighting. I found strict Voronoi tessellation was slow for global coverage. The reason is that one really has to loop over triangles, and in R that is slow. So I investigated a variant in which a triangular mesh is used, but to get the area associated with the node, the mid-point of each side is connected to the median instead of the circumcentre. That is equivalent to apportioning the triangle area equally to its three nodes. It is also equivalent to using linear finite element basis functions.

It's much quicker, because most calculation can be done on whole vectors of nodes. I found that a 9800 node global calc took nearly a minute per month to make Voronoi meshing, compared with about 2 min for a complete lat/lon grid weighting calc. That slowness isn't prohibitive, but the modified method brings it back to a few seconds per month.

So as a test I decided to revisit the just 60 stations calculation. Looking back, I was actually surprised that that calc worked as well as it did, because the stations were not evenly distributed, and there was no effectively discriminating weighting for area. The usual grid-based weighting was used, but because the sparse stations were generally one to a cell, the only differentiation was based on grid cell area, which there was just a function of latitude.

In that earlier calc I wanted to stick to a simple objective criterion for station selection. This was that the stations be rural, have at least 90 years of data, and have been still reporting in 2009. That cut the number of stations down to 61.

Now I can use the weighting in the selection process too. It's still objective - the actual temperatures are not consulted. What I do is to take a larger set (I relaxed the record length to 50 years) and mesh that. There is a big range of weights, so I pick the top two thirds (approx), the stations covering the largest areas. Then I remesh, and select again, and then repeat (stages 170, 120, 90, 60). This gets down to 60 stations, but more evenly distributed. And they are now properly weighted by area, with no missing areas.



Here is the plot of the initial selection and mesh, from 4 perspectives. The circles show (by area) the weights attached:

If it looks ragged around the rim, that's because I plotted only triangles where all nodes could be seen - otherwise there are end-of-world effects. The faint lines are the underlying mesh, and the dark lines show the area assigned to each station in the weighting.

Here are the stages of concentration, seen from N America.

And here is the final stage, from other perspectives:

and a lat/lon plot:



For comparison, here is the original 61-station selection:


Finally, here is the time series of numbers of these stations reporting in each year:

Results

Firstly. for comparison, here were the results of the original study compared with CRUTEM3 (land only. The observation was that the same general trend was followed, with of course more noise in the 61-station sample.

Now here is the new plot compared with CRUTEM3 and with GISS Land/Ocean (all to base period 1961-90): (Update - corrected following Anon comment below - the graph I originally showed is here)



It's a lot less noisy. It is also rather closer, in both trend and yearly, to GISS Land/Ocean rather than to CRUTEM3. This is interesting, because although it has 60 land stations only, they are weighted for total global coverage, and with islands heavily represented. This suggests that the difference between the two data sets may reflect weighting rather than the kind of measurement.

Next

I think the smoothing achieved with refinement by weighting worked rather well. I'll look at rationalising larger subsets that way.

I also want to look at schemes for avoiding monthly meshing. Creating these rather larger cells around a few stations means that the stations could be weighted not by individual areas but by their share in larger areas. Since several stations would inhabit such cells, frequent remeshing would be unnecessary - only required when there were no stations at all in a cell.

Area weighted trends for Antarctica

In an earlier thread I discussed the use of a least-squares program TempLS for calculating regional trends in Antarctica from mixed ground and satellite (AVHRR radiometer) data. It was in the context of recent papers by Eric Steig and colleagues (S09) and Ryan O'Donnell and colleagues(O10), where various statistical methods were used to achieve these objectives. In that analysis the stations were not (down-)weighted by their area density, which has the potential to excessively weight the Antarctic Peninsula warming, and also West Antarctica. I don't think S09 or O10 did this either, and it isn't obvious how to do it. But I've been developing Voronoi tessellation methods, and now I have been able to get them to work for this analysis. I'll also look at some residuals and explained sum of squares (R²).

Area weighting

I've talked in previous posts about the need for area density weighting. The intention is to make the summation used for averaging temperatures etc behave as integral approximations, so that in particular regions won't bias the result just because they have many stations. The clearest way to do this is to allocate a unique region of space around each station, and use that area as the weight in the least squares method. As I've also mentioned, that can lead to a locally noisy distribution of weights, and smoothing is used.

We are processing monthly averages, and so the weights should be adjusted for each month to reflect the stations that report (and not others). That means in our case a new Voronoi tessellation each month. This was done. The area weighting is done only for ground stations. Satellite stations are on a fairly uniform grid, so don't need differential weighting by area. They do, however, need a weighting relative to the ground stations and this is currently a loose parameter.

Area weighting draws attention to the question of what area we are in fact talking about. The original data set included non-Antarctic islands, including Gough Island, which at 40.4 S latitude is less Arctic than Rome. You could only weight these by the amount of ocean they cover, but is coverage of oceans intended? I excluded all the distant islands, even including Scott Island. Of course there is another question there, which is whether the boundaries of Antarctica marked on the map are appropriate for a climate analysis, or whether the boundaries sea ice areas should be included. I've stuck mostly with the map-, as did O10 and S09.

I've used a modified relative weighting factor here, as shown on the maps. It is a ratio of expected total weight for ground stations vs that for AVHRR. A high value means ground stations have more weight - a value of 1 means they are weighted equally.

Varying relative weighting

I'll start with a low weighting factor - 0.2, and using 5 principal components. This means satellite data dominates, so there is not much change from last time. Here is the map:


It looks a bit like this one, though not with warming so concentrated in the peninsula. Overall, though, the trend is a bit higher.
You'll see that I've now quoted an R² reduction. This is the relative change of sums of squares of residuals in going from accounting for local effects (basically, anomalies), to including the spatial trends, It is the ratio explained, and as you'll see, it isn't much. I'll say more about that when we look at individual resuduals. The reason it isn't much is that there is a lot of short-term variation, which dominates the SS residuals. The fact that it is small doesn't mean that it is insignificant.

Now bringing the factor up to about parity:



Now there is a difference. The pattern is somewhat similar to when I upweighted the ground stations previously, but with a more marked cooling spot. The continent trend is down, though still high. There is slightly more intense warming on the peninsula, although West Antarctica is relatively strong. And FWIW, R² is down very slightly.

So now on to letting ground stations dominate, with a factor 5.0:



Now there is even more cooling over E Antarctica, and also stronger warming over the peninsula, and strong over WA. Total warming is well down - now to 0.095 C/decade. And R² is up a bit.

I had thought R² might be used to express a preference for the value of this factor, but the changes are small and inconsistent. Overall, I think what it shows is a marked effect of the downweighting of the denser stations on the Peninsula and around the Ross Sea, and upweighting of the sparser cooling stations in the East.

Numbers of PCs

For the next test involving PCs, I'll set the weighting factor to 2.0, and use 4,5,6,7 PC's:


There are significant differences. With more PC's R² increases, as it must, and also the trend increases significantly, from about 0.1 C/decade to about 0.17 C/decade. I'm not sure what to make of that.I showed the eigenvalue plots for unweighted here; the plots now are too similar to repeat.

Residuals

To get a feel for R² and to see that the scheme is actually working, I plotted some residuals. The black curves are the residuals after subtracting the LS local temperature L. This is in effect the monthly mean. Then the red curves are what you get after taking out the predicted trend.

Clearly the noise is large, and it makes it almost impossible to see the month-to-month effect. So I have plotted also four year moving averages. These do show differences, with an apparent trend in the black residuals, which reduces with the red curves.

For individual stations, the red trend-fitted residuals need not be trendless. The extent to which they are indicates how well the PC's are serving as basis functions.

Next Steps

Now that I have the spatial weighting scheme working, I'll follow Eric's suggestion of just a time series analysis of regions, including West Antarctica. And I'll try to get the validation analysis done. There's another distraction around in that I have the Voronoi tessellation working for the whole globe, so I really want to see what differencethat makes to the indices.

New Blogger - Isaac Held

h/t Eli.

Isaac's blog is here.  Introduction here.  It looks very promising, and I wish him well. He's on the blogroll here, with RSS connection, so you'll see when there is a new post.

While I'm talking about bloggers, I've been finding the weekly index of news compiled by H.E Taylor, and hosted by Coby Beck, very useful. I made an index of posts, which I'll put below the jump. It goes into the future - H.E Taylor doesn't foretell, but if Coby keeps up his systematic numbering system, the posts should be there when the date arrives. There is a copy of the index on the document store.



An index for the posting of HE Taylors news clippings on "A few things ill considered" (Coby)

Apr 5 2009 Apr 12 2009 Apr 19 2009 Apr 26 2009 May 3 2009 May 10 2009 May 17 2009 May 24 2009 May 31 2009 Jun 7 2009 Jun 14 2009 "Jun 21 2009 Jun 28 2009 Jul 5 2009 Jul 12 2009 Jul 19 2009 Jul 26 2009 Aug 2 2009 Aug 9 2009 Aug 16 2009 Aug 23 2009 Aug 30 2009 Sep 6 2009 Sep 13 2009 Sep 20 2009 Sep 27 2009 Oct 4 2009 Oct 11 2009 Oct 18 2009 Oct 25 2009

Nov 1 2009 Nov 8 2009 Nov 15 2009 Nov 22 2009 Nov 29 2009 Dec 6 2009 Dec 13 2009 Dec 20 2009 Dec 27 2009 Jan 3 2010 Jan 10 2010 Jan 17 2010 Jan 24 2010 Jan 31 2010 Feb 7 2010 Feb 14 2010 Feb 21 2010 Feb 28 2010 Mar 7 2010 Mar 14 2010 Mar 21 2010 Mar 28 2010 Apr 4 2010 Apr 11 2010 Apr 18 2010 Apr 25 2010 May 2 2010 May 9 2010 May 16 2010

May 23 2010 May 30 2010 Jun 6 2010 Jun 13 2010 Jun 20 2010 Jun 27 2010 Jul 4 2010 Jul 11 2010 Jul 18 2010 Jul 25 2010 Aug 1 2010 Aug 8 2010 Aug 15 2010 Aug 22 2010 Aug 29 2010 Sep 5 2010 Sep 12 2010 Sep 19 2010 Sep 26 2010 Oct 3 2010 Oct 10 2010 Oct 17 2010 Oct 24 2010 Oct 31 2010 Nov 7 2010 Nov 14 2010 Nov 21 2010 Nov 28 2010 Dec 5 2010

Dec 12 2010 Dec 19 2010 Dec 26 2010 Jan 2 2011 Jan 9 2011 Jan 16 2011 Jan 23 2011 Jan 30 2011 Feb 6 2011 Feb 13 2011 Feb 20 2011 Feb 27 2011 Mar 6 2011 Mar 13 2011 Mar 20 2011 Mar 27 2011 Apr 3 2011 Apr 10 2011 Apr 17 2011 Apr 24 2011 May 1 2011 May 8 2011 May 15 2011 May 22 2011 May 29 2011 Jun 5 2011 Jun 12 2011 Jun 19 2011 Jun 26 2011

Jul 3 2011 Jul 10 2011 Jul 17 2011 Jul 24 2011 Jul 31 2011 Aug 7 2011 Aug 14 2011 Aug 21 2011 Aug 28 2011 Sep 4 2011 Sep 11 2011 Sep 18 2011 Sep 25 2011 Oct 2 2011 Oct 9 2011 Oct 16 2011 Oct 23 2011 Oct 30 2011 Nov 6 2011 Nov 13 2011 Nov 20 2011 Nov 27 2011 Dec 4 2011 Dec 11 2011 Dec 18 2011 Dec 25 2011 Jan 1 2012 Jan 8 2012 Jan 15 2012

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