Archive for August, 2019

Evidence that ERA5-based Global Temperatures Have Spurious Warming

Tuesday, August 6th, 2019

“Reading, we have a problem.”

As a followup to my post about whether July 2019 was the warmest July on record (globally-averaged), I’ve been comparing reanalysis datasets since 1979. It appears that the ERA5 reanalysis upon which WMO record temperature pronouncements are made might have a problem, with spurious warmth in recent years.

Here’s a comparison of the global-average surface air temperature variations from three reanalysis datasets: ERA5 (ECMWF), CFSv2 (NOAA/NCEP), and MERRA (NASA/GSFC). Note that only CFSv2 covers the full period, January 1979 to July 2019:

ERA5 has a substantially warmer trend than the other two. By differencing ERA5 with the other datasets we can see that there are some systematic changes that occur in ERA5, especially around 2009-2010, as well as after 1998:

These kinds of changes suggest to me differences in how the satellite deep-layer air temperatures from MSU and AMSU are handled, which through the data assimilation process can affect surface temperatures.

Reanalysis datasets’ dependence upon various data sources is difficult to diagnose because a wide variety of data are included in them: surface thermometers, weather balloons, aircraft, ships, buoys, and a variety of satellites. Because the satellites are the only truly global source, they have the potential to significantly impact global-average quantities.

If the satellites turn out to be the main reason for these differences, this means how the satellites are adjusted as the orbits decay, intercalibrated with each other, and even whether certain problematic satellites are excluded from analysis, all become significant factors in the interpretation of global average temperatures from the reanalyses.

For example, the WMO’s pronouncement that July would be at or near record warmth (which is based upon the ERA5 reanalysis) would then depend on how they did their satellite data adjustments.

So, I am no longer standing by my previous statement that the current reanalysis datasets should be used for determining record warm months. At this point, it’s not even clear to me that reanalysis datasets are better for inferring record high (or low) surface temperatures than our (UAH) satellite dataset is, even though the satellite senses much more of the troposphere than it does of the surface.

This is all very preliminary, and I am open to other interpretations.

July 2019 Was Not the Warmest on Record

Friday, August 2nd, 2019

July 2019 was probably the 4th warmest of the last 41 years. Global “reanalysis” datasets need to start being used for monitoring of global surface temperatures. [NOTE: It turns out that the WMO, which announced July 2019 as a near-record, relies upon the ERA5 reanalysis which apparently departs substantially from the CFSv2 reanalysis, making my proposed reliance on only reanalysis data for surface temperature monitoring also subject to considerable uncertainty].

We are now seeing news reports (e.g. CNN, BBC, Reuters) that July 2019 was the hottest month on record for global average surface air temperatures.

One would think that the very best data would be used to make this assessment. After all, it comes from official government sources (such as NOAA, and the World Meteorological Organization [WMO]).

But current official pronouncements of global temperature records come from a fairly limited and error-prone array of thermometers which were never intended to measure global temperature trends. The global surface thermometer network has three major problems when it comes to getting global-average temperatures:

(1) The urban heat island (UHI) effect has caused a gradual warming of most land thermometer sites due to encroachment of buildings, parking lots, air conditioning units, vehicles, etc. These effects are localized, not indicative of most of the global land surface (which remains most rural), and not caused by increasing carbon dioxide in the atmosphere. Because UHI warming “looks like” global warming, it is difficult to remove from the data. In fact, NOAA’s efforts to make UHI-contaminated data look like rural data seems to have had the opposite effect. The best strategy would be to simply use only the best (most rural) sited thermometers. This is currently not done.

(2) Ocean temperatures are notoriously uncertain due to changing temperature measurement technologies (canvas buckets thrown overboard to get a sea surface temperature sample long ago, ship engine water intake temperatures more recently, buoys, satellite measurements only since about 1983, etc.)

(3) Both land and ocean temperatures are notoriously incomplete geographically. How does one estimate temperatures in a 1 million square mile area where no measurements exist?

There’s a better way.

A more complete picture: Global Reanalysis datasets

(If you want to ignore my explanation of why reanalysis estimates of monthly global temperatures should be trusted over official government pronouncements, skip to the next section.)

Various weather forecast centers around the world have experts who take a wide variety of data from many sources and figure out which ones have information about the weather and which ones don’t.

But, how can they know the difference? Because good data produce good weather forecasts; bad data don’t.

The data sources include surface thermometers, buoys, and ships (as do the “official” global temperature calculations), but they also add in weather balloons, commercial aircraft data, and a wide variety of satellite data sources.

Why would one use non-surface data to get better surface temperature measurements? Since surface weather affects weather conditions higher in the atmosphere (and vice versa), one can get a better estimate of global average surface temperature if you have satellite measurements of upper air temperatures on a global basis and in regions where no surface data exist. Knowing whether there is a warm or cold airmass there from satellite data is better than knowing nothing at all.

Furthermore, weather systems move. And this is the beauty of reanalysis datasets: Because all of the various data sources have been thoroughly researched to see what mixture of them provide the best weather forecasts
(including adjustments for possible instrumental biases and drifts over time), we know that the physical consistency of the various data inputs was also optimized.

Part of this process is making forecasts to get “data” where no data exists. Because weather systems continuously move around the world, the equations of motion, thermodynamics, and moisture can be used to estimate temperatures where no data exists by doing a “physics extrapolation” using data observed on one day in one area, then watching how those atmospheric characteristics are carried into an area with no data on the next day. This is how we knew there were going to be some exceeding hot days in France recently: a hot Saharan air layer was forecast to move from the Sahara desert into western Europe.

This kind of physics-based extrapolation (which is what weather forecasting is) is much more realistic than (for example) using land surface temperatures in July around the Arctic Ocean to simply guess temperatures out over the cold ocean water and ice where summer temperatures seldom rise much above freezing. This is actually one of the questionable techniques used (by NASA GISS) to get temperature estimates where no data exists.

If you think the reanalysis technique sounds suspect, once again I point out it is used for your daily weather forecast. We like to make fun of how poor some weather forecasts can be, but the objective evidence is that forecasts out 2-3 days are pretty accurate, and continue to improve over time.

The Reanalysis picture for July 2019

The only reanalysis data I am aware of that is available in near real time to the public is from WeatherBell.com, and comes from NOAA’s Climate Forecast System Version 2 (CFSv2).

The plot of surface temperature departures from the 1981-2010 mean for July 2019 shows a global average warmth of just over 0.3 C (0.5 deg. F) above normal:

Note from that figure how distorted the news reporting was concerning the temporary hot spells in France, which the media reports said contributed to global-average warmth. Yes, it was unusually warm in France in July. But look at the cold in Eastern Europe and western Russia. Where was the reporting on that? How about the fact that the U.S. was, on average, below normal?

The CFSv2 reanalysis dataset goes back to only 1979, and from it we find that July 2019 was actually cooler than three other Julys: 2016, 2002, and 2017, and so was 4th warmest in 41 years. And being only 0.5 deg. F above average is not terribly alarming.

Our UAH lower tropospheric temperature measurements had July 2019 as the third warmest, behind 1998 and 2016, at +0.38 C above normal.

Why don’t the people who track global temperatures use the reanalysis datasets?

The main limitation with the reanalysis datasets is that most only go back to 1979, and I believe at least one goes back to the 1950s. Since people who monitor global temperature trends want data as far back as possible (at least 1900 or before) they can legitimately say they want to construct their own datasets from the longest record of data: from surface thermometers.

But most warming has (arguably) occurred in the last 50 years, and if one is trying to tie global temperature to greenhouse gas emissions, the period since 1979 (the last 40+ years) seems sufficient since that is the period with the greatest greenhouse gas emissions and so when the most warming should be observed.

So, I suggest that the global reanalysis datasets be used to give a more accurate estimate of changes in global temperature for the purposes of monitoring warming trends over the last 40 years, and going forward in time. They are clearly the most physically-based datasets, having been optimized to produce the best weather forecasts, and are less prone to ad hoc fiddling with adjustments to get what the dataset provider thinks should be the answer, rather than letting the physics of the atmosphere decide.

UAH Global Temperature Update for July, 2019: +0.38 deg. C

Thursday, August 1st, 2019

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for July, 2019 was +0.38 deg. C, down from the June, 2019 value of +0.47 deg. C:

The linear warming trend since January, 1979 remains at +0.13 C/decade.

Various regional LT departures from the 30-year (1981-2010) average for the last 19 months are:

YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST
2018 01 +0.29 +0.51 +0.06 -0.10 +0.70 +1.39 +0.52
2018 02 +0.24 +0.28 +0.21 +0.05 +0.99 +1.21 +0.35
2018 03 +0.28 +0.43 +0.12 +0.08 -0.19 -0.32 +0.76
2018 04 +0.21 +0.32 +0.09 -0.14 +0.06 +1.02 +0.84
2018 05 +0.16 +0.38 -0.05 +0.01 +1.90 +0.14 -0.24
2018 06 +0.20 +0.33 +0.06 +0.12 +1.11 +0.76 -0.41
2018 07 +0.30 +0.38 +0.22 +0.28 +0.41 +0.24 +1.49
2018 08 +0.18 +0.21 +0.16 +0.11 +0.02 +0.11 +0.37
2018 09 +0.13 +0.14 +0.13 +0.22 +0.89 +0.23 +0.27
2018 10 +0.20 +0.27 +0.12 +0.30 +0.20 +1.08 +0.43
2018 11 +0.26 +0.24 +0.27 +0.45 -1.16 +0.68 +0.55
2018 12 +0.25 +0.35 +0.15 +0.30 +0.25 +0.69 +1.20
2019 01 +0.38 +0.35 +0.41 +0.35 +0.53 -0.15 +1.15
2019 02 +0.37 +0.47 +0.28 +0.43 -0.02 +1.04 +0.05
2019 03 +0.34 +0.44 +0.25 +0.41 -0.55 +0.96 +0.58
2019 04 +0.44 +0.38 +0.51 +0.53 +0.50 +0.92 +0.91
2019 05 +0.32 +0.29 +0.35 +0.39 -0.61 +0.98 +0.38
2019 06 +0.47 +0.42 +0.52 +0.64 -0.64 +0.90 +0.35
2019 07 +0.38 +0.33 +0.44 +0.45 +0.11 +0.33 +0.87

The UAH LT global anomaly image for July, 2019 should be available in the next few days here.

The global and regional monthly anomalies for the various atmospheric layers we monitor should be available in the next few days at the following locations:

Lower Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt
Mid-Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0/tmt/uahncdc_mt_6.0.txt
Tropopause: http://vortex.nsstc.uah.edu/data/msu/v6.0/ttp/uahncdc_tp_6.0.txt
Lower Stratosphere: http://vortex.nsstc.uah.edu/data/msu/v6.0/tls/uahncdc_ls_6.0.txt