The global average lower tropospheric temperature anomaly for July (+0.28 °C) was down from June 2012 (+0.37 °C). Click on the image for the full-size version:

Here are the monthly stats:

YR MON GLOBAL NH SH TROPICS
2011 01 -0.01 -0.06 +0.04 -0.37
2011 02 -0.02 -0.04 +0.00 -0.35
2011 03 -0.10 -0.07 -0.13 -0.34
2011 04 +0.12 +0.20 +0.04 -0.23
2011 05 +0.13 +0.15 +0.12 -0.04
2011 06 +0.32 +0.38 +0.25 +0.23
2011 07 +0.37 +0.34 +0.40 +0.20
2011 08 +0.33 +0.32 +0.33 +0.16
2011 09 +0.29 +0.30 +0.27 +0.18
2011 10 +0.12 +0.17 +0.06 -0.05
2011 11 +0.12 +0.08 +0.17 +0.02
2011 12 +0.13 +0.20 +0.06 +0.04
2012 1 -0.09 -0.06 -0.12 -0.14
2012 2 -0.11 -0.01 -0.21 -0.28
2012 3 +0.11 +0.13 +0.09 -0.11
2012 4 +0.30 +0.41 +0.19 -0.12
2012 5 +0.29 +0.44 +0.14 +0.03
2012 6 +0.37 +0.54 +0.20 +0.14
2012 7 +0.28 +0.44 +0.11 +0.33

As a reminder, the most common reason for large month-to-month swings in global average temperature is small fluctuations in the rate of convective overturning of the troposphere, discussed here.

This is meant to be just a heads up that we have submitted a paper to Journal of Geophysical Research (JGR) which I think is quite significant. We used a 1D forcing-feedback-diffusion model of ocean temperature change to 2,000 meters depth to explain ocean temperature variations measured since 1955.

We ask the question: What combination of (1) forcings, (2) feedback (climate sensitivity), and (3) ocean diffusion (vertical mixing) best explain the Levitus global-average ocean temperature trends since 1955? These are the three main processes which control global-average surface temperatures on longer time scales (a point which has also been made by NASA’s James Hansen).

The 1D model has the advantage that it conserves energy, which apparently is still a problem with the IPCC 3D models which exhibit spurious temperature trends (peer reviewed paper here). Our own analysis has shown that at least 3 of the IPCC models actually produce net (full-depth) ocean cooling despite positive radiative forcing over the 2nd half of the 20th Century.

After all, if a climate model can’t even satisfy the 1st Law of Thermodynamics, and global warming is fundamentally a conservation of energy process (net accumulation of energy leads to warming), how then can 3D models be used to explain or predict climate change? I don’t see how the IPCC scientific community continues to avoid mass cognitive dissonance.

The primary forcing used in our model is basically the same as that used in the new CMIP5 experiments, the largest components of which are anthropogenic greenhouse gases and aerosols, and volcanic aerosols. Using these traditional forcings alone in our 1D model gives a climate sensitivity in the range of what the IPCC models produce.

But an important additional component of our model is the observed history of the El Nino/Southern Oscillation (ENSO) as a pseudo-forcing, both through changes in ocean mixing across the thermocline (ENSO’s primary influence), and through potential changes in global albedo preceding ENSO temperature changes. These pseudo-forcings are included only to the extent they help to explain the Levitus ocean temperature data, as well as explain the satellite-observed relationship between radiative flux variations and sea surface temperature.

The results are, shall we say, not as supportive of the IPCC view of the climate system as the IPCC might like; more frequent El Ninos since the late 1970s do impact our interpretation of climate sensitivity and the causes of climate change. The paper also serves as a response to Andy Dessler’s published criticisms of our feedback work.

A shorter version of the paper was first submitted to Geophysical Research Letters (GRL) a few weeks ago, and was rejected outright by the editor as not being appropriate for GRL (!), a claim which seems quite strange indeed. I suspect the editor was trying to avoid the kind of controversy which led to the resignation of the editor of the journal Remote Sensing after publication of a previous paper of ours.

Now we shall see whether it is possible for JGR to provide an unbiased peer review. If our paper is rejected there as well, we might post the paper here so anyone can judge for themselves whether the study has merit.

I know that many journalists who lived through the recent heat wave in the East think the event somehow validates global warming theory, but I’m sorry: It’s summer. Heat waves happen. Sure, many high temperature records were broken, but records are always being broken.

And the strong thunderstorms that caused widespread power outages? Ditto.

Regarding the “thousands” of broken records, there are not that many high-quality weather observing stations that (1) operated since the record warm years in the 1930s, and (2) have not been influenced by urban heat island effects, so it’s not at all obvious that the heat wave was unprecedented. Even if it was the worst in the last century for the Eastern U.S. (before which we can’t really say anything), there is no way to know if it was mostly human-caused or natural, anyway.

“But, Roy, the heat wave is consistent with climate model predictions!”. Yeah, well, it’s also consistent with natural weather variability. So, take your pick.

For the whole U.S. in June, average temperatures were not that remarkable. Here are the last 40 years from my population-adjusted surface temperature dataset, and NOAA’s USHCN (v2) dataset (both based upon 5 deg lat/lon grid averages; click for large version):

Certainly the U.S drought conditions cannot compare to the 1930s.

I really tire of the media frenzy which occurs when disaster strikes…I’ve stopped answering media inquiries. Mother Nature is dangerous, folks. And with the internet and cell phones, now every time there is a severe weather event, everyone in the world knows about it within the hour. In the 1800s, it might be months before one part of the country found out about disaster in another part of the country. Sheesh.

The global average lower tropospheric temperature anomaly for June (+0.37 °C) was up from May 2012 (+0.29 °C). Click on the image for the super-sized version:

The 4th order polynomial fit to the data (courtesy of Excel) is for entertainment purposes only, and should not be construed as having any predictive value whatsoever.

Here are the monthly stats:

YR MON GLOBAL NH SH TROPICS
2011 01 -0.010 -0.055 +0.036 -0.372
2011 02 -0.020 -0.042 +0.002 -0.348
2011 03 -0.101 -0.073 -0.128 -0.342
2011 04 +0.117 +0.195 +0.039 -0.229
2011 05 +0.133 +0.145 +0.121 -0.043
2011 06 +0.315 +0.379 +0.250 +0.233
2011 07 +0.374 +0.344 +0.404 +0.204
2011 08 +0.327 +0.321 +0.332 +0.155
2011 09 +0.289 +0.304 +0.274 +0.178
2011 10 +0.116 +0.169 +0.062 -0.054
2011 11 +0.123 +0.075 +0.170 +0.024
2011 12 +0.126 +0.197 +0.055 +0.041
2012 1 -0.089 -0.058 -0.120 -0.137
2012 2 -0.111 -0.014 -0.209 -0.276
2012 3 +0.111 +0.129 +0.094 -0.106
2012 4 +0.299 +0.413 +0.185 -0.117
2012 5 +0.292 +0.444 +0.141 +0.033
2012 6 +0.369 +0.540 +0.199 +0.140

As a reminder, the most common reason for large month-to-month swings in global average temperature is small fluctuations in the rate of convective overturning of the troposphere, discussed here.

Yesterday, the Japan Aerospace Exploration Agency (JAXA) released first-light imagery from the new AMSR2 instrument on JAXA’s GCOM-W1 satellite (“Shizuku”), which replaces the AMSR-E instrument which failed last fall on NASA’s Aqua satellite after 9+ years of observation.

The Shizuku satellite has been successfully boosted into the NASA A-Train satellite constellation, and the AMSR2 spin rate has been increased to its operational value of 40 rpm.

The following two images are not meant to be science-quality, only to demonstrate the instrument is operating as expected:

Operational products from AMSR2 should start flowing in August.

The recent thunderstorm wind event which caused widespread wind damage from Ohio to the mid-Atlantic coast has, rather predictably, led to claims that global warming is the root cause.

Known as a “derecho“, these events are indeed uncommon, but have always been around: the term was originally coined in 1888 in a study of thunderstorm wind damage which occurred in 1877.

In fact, one of the most famous events occurred when global temperatures reached a minimum, back in the 1970’s. Known simply as “The Storm”, it occurred 35 years ago today, on July 4, 1977. There were widespread blowdowns of trees (see the photo, above). Even though the event occurred over relatively unpopulated areas in Minnesota, Wisconsin, and Michigan, the highest recorded wind speed was an astonishing 115 mph, officially recorded with an airport anemometer.

Compare that to the derecho event of last week, which occurred over heavily populated areas: the highest measured wind speed in the extensive list of reports at the Storms Prediction Center was only 92 mph, and even that was on a home weather station, and so is unofficial.

So, why all the fuss over last weeks storm? Because it didn’t hit flyover country. Tens of millions of people were affected, and millions went without power.

Of course, those affected included many journalists, so it is only natural that they would speculate (and seek out experts to speculate) about the sinister causes of such an event.

Surely the silliest comment I saw came from Bill Nye, “The Science Guy”, who stated: “…We had a 30-degree temperature drop in Maryland and Virginia this weekend, in just – in a half-hour. These are consistent with climate models.”

First of all, such temperature drops occur routinely with the passage of mid-latitude thunderstorms. Secondly, climate models predict no such thing anyway. If “The Science Guy” gets it this wrong, how can I trust him on anything else?

The U.S. lower-48 surface temperature anomaly from my population density-adjusted (PDAT) dataset was 1.26 deg. C above the 1973-2012 average for May 2012, with a 1973-2012 linear warming trend of +0.14 deg. C/decade (click for full-size version):

The corresponding USHCN anomaly computed relative to the same base period was +1.65 deg. C, with nearly double my warming trend (+0.27 deg. C/decade). The warming of the USHCN relative to my dataset shows that most of the discrepancy arises during the 1996-98 period:

Despite the weaker warming trend in my dataset, Spring 2012 still ranks as the warmest spring since the beginning of my record (1973). The 12-month period ending in May 2012 is also the warmest 12-month period in the record.

Due to a lack of station data and uncertainties regarding urban heat island (UHI) effects, I have no opinion on how the recent warmth compares to, say, the 1930s. There is also no guarantee that my method for UHI adjustment since 1973 has done a sufficient job of removing UHI effects. A short description of the final procedure I settled on for population density adjustment of the surface temperatures can be found here.

The global average lower tropospheric temperature anomaly for May 2012 (+0.29 °C) changed little from April (+0.30 °C), with some warming to near-average temperatures in the tropics being balanced by a little cooling in the Southern Hemisphere (click on the image for the full-size version):

The 4th order polynomial fit to the data (courtesy of Excel) is for entertainment purposes only, and should not be construed as having any predictive value whatsoever.

Here are the monthly stats:

YR MON GLOBAL NH SH TROPICS
2011 01 -0.010 -0.055 +0.036 -0.372
2011 02 -0.020 -0.042 +0.002 -0.348
2011 03 -0.101 -0.073 -0.128 -0.342
2011 04 +0.117 +0.195 +0.039 -0.229
2011 05 +0.133 +0.145 +0.121 -0.043
2011 06 +0.315 +0.379 +0.250 +0.233
2011 07 +0.374 +0.344 +0.404 +0.204
2011 08 +0.327 +0.321 +0.332 +0.155
2011 09 +0.289 +0.304 +0.274 +0.178
2011 10 +0.116 +0.169 +0.062 -0.054
2011 11 +0.123 +0.075 +0.170 +0.024
2011 12 +0.126 +0.197 +0.055 +0.041
2012 1 -0.089 -0.058 -0.120 -0.137
2012 2 -0.111 -0.014 -0.209 -0.276
2012 3 +0.111 +0.129 +0.094 -0.106
2012 4 +0.299 +0.413 +0.185 -0.117
2012 5 +0.289 +0.439 +0.139 +0.033

As a reminder, the most common reason for large month-to-month swings in global average temperature is small fluctuations in the rate of convective overturning of the troposphere, discussed here.

UPDATE: AMSR2 has been successfully spun up to its initial spin rate of 4 rpm (it will later be spun up to 40 rpm for normal operations). It will take 45 days for the Shinzuku satellite to be maneuvered into the A-Train satellite constellation; about 3 months for complete instrument checkout.

Now this is a real treat…the first on-orbit image I am aware of taken from the spacecraft of an Earth-observation instrument ON the spacecraft itself (all imagery courtesy of JAXA).

The first image is an artist’s rendering of the Japanese Shizuku satellite (aka GCOM-W) with the location of the camera indicated:

The second is an actual photo from that camera of the AMSR2 instrument after its antenna has been deployed from its stowed position:

I’m not sure of the schedule regarding when calibrated data will start flowing operationally. The calibration design of AMSR2 has been improved, and I’m sure JAXA will want to be pretty confident of their calibration before data are released. Then, the algorithms which convert the calibrated data into products such as sea ice parameters and SST will have to be tested.

Well before those events happen, though, I’m sure we will see some “first light” imagery from the instrument.

A variety of Shizuku photos and artist renderings are available here.

UPDATE: As of 12:00 pm CDT, the launch looked to be successful. All ascent stages have separated, and the Korean satellite (the 2nd payload) has separated from the GCOM-W satellite.

The AMSR2 instrument on the Japanese GCOM-W satellite (now named Shizuku) is scheduled for launch this morning. The 3 minute launch window starts at 11:39 a.m. CDT (a nighttime launch in Japan). The H2A rocket has been moved to the launch pad and fueling is underway.

Spaceflight Now is providing live updates. Live video coverage is supposed to be available here starting 1/2 hour before launch…as of right now, the links are not operable.
The Shizuku satellite will join the NASA A-Train satellite constellation, which includes the Aqua satellite. You might recall that the AMSR-E instrument on Aqua failed last fall after 9+ years of successful operation.

Like AMSR-E, AMSR2 will be used to measure twice-daily sea surface temperatures (even through clouds), sea ice, precipitation rates, columnar water vapor and cloud water contents over the ocean, surface wind speeds over the ocean, and snow cover.