Catastrophic mesoscale convective system storms in the Sahel now three times more likely

Climate change brings more Sahel storms

Climate change is upsetting rainfall patterns and the frequency of flooding in West Africa as it makes the region's Sahel storms three times likelier.

by Tim Radford, Climate News Network, May 7, 2017

LONDON – Climate change has already made a difference to life in the West African Sahel, the arid belt of land stretching from the Atlantic to the Red Sea which separates the Sahara desert from the African savanna. It has made catastrophic storms three times more frequent.

And, according to a new study in the journal Nature, Sahel storms are among the most powerful on the planet. In 2009, one vast downpour deposited 263 mm of rain over Ouagadougou, the capital of Burkina Faso, claiming 8 lives, flooding half the city and forcing 150,000 people out of their homes.

Researchers believe the pattern of thunderstorms known as mesoscale convective systems will increase in frequency as global temperatures rise, as a consequence of increasing levels of carbon dioxide in the atmosphere, in turn driven by worldwide use of fossil fuels as sources of energy.

Mesoscale convective systems are big, bad, and very cold columns of thunderous cloud: up to 16 km high, covering an area of 25,000 square kilometres, and with temperatures at the highest altitude as low as minus 40 °C.

Between 1986 and 2005, Burkina Faso registered floods at a rate of little more than one a year. In the 11 years between 2006 and 2016, it was hit by 55 flood events.

Repeated warnings

Climate scientists have been warning for three decades that global warming will be accompanied by an increase in “extreme” events: in particular drought, flood, heat wave, and tropical cyclone.

Global warming has already been observed in the Sahel, and the consequences have not necessarily been bad: overall, precipitation has increased, and farmers have benefited, although in a dryland region south of the Sahara where people have endured a 2,000-year history of periodic drought, famine remains a constant hazard.

And now, so do massive downpours of rain: the Sahel storms. British and French scientists examined 35 years of satellite data and the rain gauges in the region to identify a rise in extreme daily rainfall totals. They found 85% of extreme rainfall cases coincided with satellite records of a passing mesoscale convection system.

They also examined the pattern of temperatures over the region and found that although the annual average temperatures have risen, the so-called “wet season” temperatures have remained steady. That is, locally warmer conditions alone have not brought more rainfall.

“Global warming is expected to produce more intense storms, but we were shocked to see the speed of changes taking place in this region of Africa”

Instead, they blame man-made global warming which has changed wind and rain conditions, and this will go on strengthening during this century, “suggesting the Sahel will experience particularly marked increases in extreme rain,” they conclude.

“Global warming is expected to produce more intense storms, but we were shocked to see the speed of changes taking place in this region of Africa,” said Christopher Taylor, a meteorologist at the UK’s Centre for Ecology and Hydrology, who led the study.

His co-author Douglas Parker, professor of meteorology at the University of Leeds in the UK, said: “African storms are highly organised meteorological engines, whose currents extract water from the air to produce torrential rain.

“We have seen these engines becoming more efficient over recent decades, with resulting increases in the frequency of hazardous events.” 

http://climatenewsnetwork.net/climate-change-brings-more-sahel-storms

RealClimate: Hottest years rankings: (1) 2010, (2) 2005, (3) 2007/1998, (4) 2013/2009/2003/2002, (5) 2013/2006/2003/1998

by Stefan Rahmstorf, RealClimate, January 27, 2014

The global temperature data for 2013 are now published. 2010 and 2005 remain the warmest years since records began in the 19th century. 1998 ranks third in two records, and in the analysis of Cowtan and Way, which interpolates the data-poor region in the Arctic with a better method, 2013 is warmer than 1998 (even though 1998 was a record El Nino year, and 2013 was neutral).

The end of January, when the temperature measurements of the previous year are in, is always the time to take a look at the global temperature trend. (And, as the Guardian noted aptly, also the time where the “climate science denialists feverishly yell [...] that global warming stopped in 1998.”) Here is the ranking of the warmest years in the four available data sets of the global near-surface temperatures (1):

Rank	NASA GISS	NOAA NCDC	HadCRUT4	Cowtan & Way
1	2010	2010	2010	2010
2	2005	2005	2005	2005
3	2007	1998	1998	2007
4	2002	2013	2003	2009
5	1998	2003	2006	2013

New this year: for the first time there is a careful analysis of geographical data gaps – especially in the Arctic there’s a gaping hole – and their interpolation for the HadCRUT4 data. Thus there are now two surface temperature data sets with global coverage (the GISTEMP data from NASA have always filled gaps by interpolation). In these two data series 2007 is ranked 3rd. Their direct comparison is shown in the figure below.

Figure 1 Global temperature (annual values) in the data from NASA GISS (orange) and from Cowtan & Way (blue), i.e., HadCRUT4 with interpolated data gaps.

One can clearly see the extreme year 1998, which (thanks to the record-El Niño) stands out above the long-term trend like no other year. But even taking this outlier year as starting point, the linear trend 1998-2013 in all four data sets is positive. Also clearly visible is 2010 as the warmest year since records began, and the minima in the years 2008 and 2011/2012. But just like the peaks are getting higher, these minima are less and less deep.

In these data curves I cannot see a particularly striking or significant current “warming pause”, even though the warming trend from 1998 is of course less than the long-term trend. Even in Nature, there was recently a (journalistic) contribution that in its introduction strongly overstated this alleged “hiatus”. It makes a good story that perhaps some cannot resist. (“Warming trend is somewhat reduced, but within the usual range of variation” simply does not make good headline.)

The role of El Niño and La Niña

The recent slower warming is mainly explained by the fact that in recent years the La Niña state in the tropical Pacific prevailed, in which the eastern Pacific is cold and the ocean stores more heat (2). This is due to an increase in the trade winds that push water westward across the tropical Pacific, while in the east cold water from the depths comes to the surface (see last graph here). In addition, radiative forcing has recently increased more slowly (more on this in the analysis of Hansen et al. – definitely worth a read).

NASA shows the following graphic, where you can see that the warmer years tend to be those with an El Niño in the tropical Pacific (red years), while the particularly cool years are those with La Niña (blue years).

Figure 2. The GISS data, with El Niño and La Niña conditions highlighted. Neutral years like 2013 are gray. Source: NASA.

Quality of the interpolation

How good is the interpolation into regions not regularly covered by weather stations? In any case, of course, better than simply ignoring the gaps, as the HadCRUT and NOAA data have done so far.  The truly global average is important, since only it is directly related to the energy balance of our planet and thus the radiative forcing by greenhouse gases. An average over just part of the globe is not.  The Arctic has been warming disproportionately in the last ten to fifteen years.

But how well the interpolation works we know only since the important work of Cowtan and Way. These colleagues have gone to the trouble of carefully validating their method. Although there are no permanent weather stations in the Arctic, there is intermittent data from buoys and and from weather model reanalyses with which they could test their method. For the last few decades and Cowtan and Way also make use of satellite data (more on this in our article on underestimated warming). I therefore assume that the data from Cowtan and Way is the methodologically best estimate of the global mean temperature which we currently have. This correction is naturally small (less than a tenth of a degree) and hardly changes the long-term trend of global warming – but if you look deeper into  shorter periods of time, it can make a noticeable difference. The comparison with the uncorrected HadCRUT4 data is shown in the figure below.

Figure 3. Comparison of interpolated and non-interpolated HadCRUT4 data, as moving averages over 12 months. Source: Kevin Cowtan, University of York.

And here’s a look at the last years in detail:

Figure 4. The interpolated HadCRUT4 data (annual average) from 1970. Source: Kevin Cowtan, University of York.

Following this analysis, 2013 was thus even warmer than the record El-Niño-year 1998.

Conclusion

• In all four data series of the global near-surface air temperature, the linear trend even from the extreme El Niño year 1998 is positive, i.e. shows continued warming, despite the choice of a warm outlier as the initial year.
• In all four data series of the global near-surface air temperature, 2010 was the warmest year on record, followed by 2005.
• The year 1998 is, at best, rank 3 – in the currently best data set of Cowtan & Way, 1998 is actually only ranked 7th. Even 2013 is – without El Niño – warmer there than 1998.

The German news site Spiegel Online presents these facts under the headline Warming of the air paused for 16 years (my translation). The headline of the NASA news release, NASA Finds 2013 Sustained Long-Term Climate Warming trend, is thus completely turned on its head.

This will not surprise anyone who has followed climate reporting of Der Spiegel in recent years. To the contrary – colleagues express their surprise publicly when a sensible article on the subject appears there. For years, Der Spiegel has acted as a gateway for dubious “climate skeptics” claims into the German media whilst trying to discredit top climate scientists (we’ve covered at least one example here).

Do Der Spiegel readers know more (as their advertising goes) – more than NASA, NOAA, Hadley Centre and the World Meteorological Organization WMO together? Or are they simply being taken for a ride for political reasons?

Footnotes

(1) In addition to the data of the near-surface temperatures, which are composed of measurements from weather stations and sea surface temperatures, there is also the microwave data from satellites, which can be used to estimate air temperatures in the troposphere in a few kilometers altitude. In the long-term climate trend since the beginning of satellite measurements in 1979, the tropospheric temperatures show a similar warming as the surface temperatures, but the short-term fluctuations in the troposphere are significantly different from those near the surface. For example, the El Niño peak in 1998 is about twice as high as in the surface data in the troposphere, see Foster and Rahmstorf 2011. In their trend from 1998 , the two satellite series contradict each other: UAH shows +0.05 °C per decade (a bit more than HadCRUT4), RSS shows -0.05 °C per decade.

(2) Another graphic (Figure 5) illustrates the change between El Niño and La Niña: the Oceanic Niño Index ONI, the standard index of NOAA to describe the seesaw in the tropical Pacific.

Figure 5. The ONI index. The arrows added by me point to some of the globally warm or cool years (compare Figure 1 or 4). Source: NOAA.

Weblinks

The global temperature jigsaw (an overview over the “pause” debate)
What ocean heating reveals about global warming

http://www.realclimate.org/index.php/archives/2014/01/global-temperature-2013/

WMO: 2001-2010 warmest decade since records began in 1850

2001-2010 warmest decade on record: WMO

AFP, March 23, 2012

GENEVA — Climate change has accelerated in the past decade, the UN weather agency said Friday, releasing data showing that 2001 to 2010 was the warmest decade on record.

The 10-year period was also marked by extreme levels of rain or snowfall, leading to significant flooding on all continents, while droughts affected parts of East Africa and North America.

"The decade 2001-2010 was the warmest since records began in 1850, with global land and sea surface temperatures estimated at 0.46 degrees Celsius above the long term average of 14.0 degrees Celsius (57.2 degrees Fahrenheit)," said the World Meteorological Organisation.

Nine of the 10 years also counted among the 10 warmest on record, it added, noting that "climate change accelerated" during the first decade of the 21st century.

The trend continued in 2011, which was the warmest year on record despite La Nina -- a weather pattern which has a cooling effect.

The average temperature in 2011 was 0.40 degrees Celsius above the long term average, said the WMO.

"This 2011 annual assessment confirms the findings of the previous WMO annual statements that climate change is happening now and is not some distant future threat," said WMO Secretary-General Michel Jarraud.

"The world is warming because of human activities and this is resulting in far-reaching and potentially irreversible impacts on our Earth, atmosphere and oceans," he added.

The UN weather agency noted that during the decade, "numerous weather and climate extremes affected almost every part of the globe with flooding, droughts, cyclones, heat waves and cold waves."

Historical floods hit Eastern Europe in 2001 and 2005, Africa in 2008, Asia and Australia in 2010. Global precipitation -- including rain or snow -- reached the second highest average since 1901. The highest average was recorded for the decade 1951-1960.

Meanwhile for the North Atlantic basin, the 10 years marked the highest level of tropical cyclone activity, including Hurricane Katrina which struck the United States in 2005 and Cyclone Nargis which hit Myanmar in 2008.

http://www.google.com/hostednews/afp/article/ALeqM5hqv_q4RxB9yyysWL-JvMrUjSfHYg

Menne et al., JGR 2010: On the reliability of the U.S. Surface Temperature Record

On the reliability of the U.S. Surface Temperature Record

by John Cook, Skeptical Science, January 22, 2010

The website surfacestations.org enlisted an army of volunteers, travelling across the U.S. photographing weather stations. The point of this effort was to document cases of microsite influence -- weather stations located near car parks, air conditioners and airport tarmacs and anything else that might impose a warming bias. While photos can be compelling, the only way to quantify any microsite influence is through analysis of the data. This has been done in On the reliability of the U.S. Surface Temperature Record (Memme 2010), published in the Journal of Geophysical Research. The trends from poorly sited weather stations are compared to well-sited stations. The results indicate that yes, there is a bias associated with poor exposure sites. However, the bias is not what you expect.

Weather stations are split into two categories: good (rating 1 or 2) and bad (ratings 3, 4 or 5). Each day, the minimum and maximum temperature are recorded. All temperature data goes through a process of homogenisation, removing non-climatic influences such as relocation of the weather station or change in the Time of Observation. In this analysis, both the raw, unadjusted data and homogenised, adjusted data are compared. Figure 1 shows the comparison of unadjusted temperature from the good and bad sites. The top figure (c) is the maximum temperature, the bottom figure (d) is the minimum temperature. The black line represents well sited weather stations with the red line representing poorly sited stations.

Figure 1. Annual average maximum and minimum unadjusted temperature change calculated using (c) maximum and (d) minimum temperatures from good and poor exposure sites (Menne 2010).

Poor sites show a cooler maximum temperature compared to good sites. For minimum temperature, the poor sites are slightly warmer. The net effect is a cool bias in poorly sited stations. Considering all the air-conditioners, BBQs, car parks and tarmacs, this result is somewhat a surprise. Why are poor sites showing a cooler trend than good sites?

The cool bias occurs primarily during the mid and late 1980s. Over this period, about 60% of USHCN sites converted from Cotton Region Shelters (CRS otherwise known as Stevenson Screens) to electronic Maximum/Minimum Temperature Systems (MMTS). MMTS sensors are attached by cable to an indoor readout device. Consequently, limited by cable length, they're often located closer to heated buildings, paved surfaces and other artificial sources of heat.

Investigations into the impact of the MMTS on temperature data have found that on average, MMTS sensors record lower daily maximums than their CRS counterparts, and, conversely, slightly higher daily minimums (Menne 2009). Only about 30% of the good sites currently have the newer MMTS-type sensors compared to about 75% of the poor exposure locations. Thus, it is the MMTS sensors that are responsible for the cool bias imposed on poor sites.

When the change from CRS to MMTS are taken into account, as well as other biases such as station relocation and Time of Observation, the trend from good sites show close agreement with poor sites.

Figure 2: Comparison of U.S. average annual (a) maximum and (b) minimum temperatures calculated using USHCN version 2 adjusted temperatures. Good and poor site ratings are based on surfacestations.org.

Does this latest analysis mean all the work at surfacestations.org has been a waste of time? On the contrary, the laborious task of rating each individual weather station enabled Memme 2010 to identify a cool bias in poor sites and isolate the cause. The role of surfacestations.org is recognised in the paper's acknowledgements in which they "wish to thank Anthony Watts and the many volunteers at surfacestations.org for their considerable efforts in documenting the current site characteristics of USHCN stations."

A net cooling bias was perhaps not the result the surfacestations.org volunteers were hoping for but improving the quality of the surface temperature record is surely a result we should all appreciate.

Link:  http://www.skepticalscience.com/On-the-reliability-of-the-US-Surface-Temperature-Record.html

James Hansen, Reto Ruedy, Makiko Sato, Ken Lo: If It’s That Warm, How Come It’s So Damned Cold?

If it’s that warm, how come it’s so damned cold?

James Hansen, Reto Ruedy, Makiko Sato, Ken Lo

The past year, 2009, tied as the second warmest year in the 130 years of global instrumental temperature records, in the surface temperature analysis of the NASA Goddard Institute for Space Studies (GISS). The Southern Hemisphere set a record as the warmest year for that half of the world.

Global mean temperature, as shown in Figure 1a, was 0.57 °C (1.0 °F) warmer than climatology (the 1951‐1980 base period). Southern Hemisphere mean temperature, as shown in Figure 1b, was 0.49 °C (0.88 °F) warmer than in the period of climatology.

See link for figures -- sorry I cannot copy them from the pdf file -- drat!!!

http://www.columbia.edu/~jeh1/mailings/2010/20100127_TemperatureFinal.pdf
Figure 1. (a) GISS analysis of global surface temperature change. Green vertical bar is estimated 95% confidence range (two standard deviations) for annual temperature change. (b) Hemispheric
temperature change in GISS analysis. (Base period is 1951–1980. This base period is fixed consistently
in GISS temperature analysis papers – see References. Base period 1961–1990 is used for comparison
with published HadCRUT analyses in Figures 3 and 4.)

The global record warm year, in the period of near‐global instrumental measurements (since the late 1800s), was 2005. Sometimes it is asserted that 1998 was the warmest year.

The origin of this confusion is discussed below.

There is a high degree of interannual (year‐to‐year) and decadal variability in both global and hemispheric temperatures. Underlying this variability, however, is a long‐term warming trend that has become strong and persistent over the past three decades.

The long‐term trends are more apparent when temperature is averaged over several years. The 60‐month (5‐year) and 132 month (11‐year) running mean temperatures are shown in Figure 2 for the globe and the hemispheres. The 5‐year mean is sufficient to reduce the effect of the El Nino–La Nina cycles of tropical climate. The 11‐year mean minimizes the effect of solar variability – the brightness of the sun varies by a measurable amount over the sunspot cycle, which is typically of 10–12 years' duration.

Complete paper at this link: http://www.columbia.edu/~jeh1/mailings/2010/20100127_TemperatureFinal.pdf

Dlugokencky et al., GRL (September 2009): Observational constraints on recent increases in the atmospheric CH4 burden

Geophysical Research Letters, 36 (2009) L18803; doi: 10.1029/2009GL039780.

Observational constraints on recent increases in the atmospheric CH₄ burden 

 E. J. Dlugokencky, L. Bruhwiler (NOAA Earth System Research Laboratory, Boulder, CO, U.S.A.), J. W. C. White (INSTAAR, University of Colorado, Boulder, CO, U.S.A.), L. K. Emmons (National Center for Atmospheric Research, Boulder, CO, U.S.A.), P. C. Novelli, S. A. Montzka, K. A. Masarie, P. M. Lang, A. M. Crotwell, J. B. Miller (NOAA Earth System Research Laboratory, Boulder, CO, U.S.A.) and L. V. Gatti (Divisao de Quimica Ambiental, Laboratorio de Quimica Atmosferica, Insituto de Pesquisas Energéticas e Nucleares, São Paulo, Brazil )

Received 6 July 2009; accepted 18 August 2009; published 17 September 2009. 

Abstract

Measurements of atmospheric CH₄ from air samples collected weekly at 46 remote surface sites show that, after a decade of near-zero growth, globally averaged atmospheric methane increased during 2007 and 2008. During 2007, CH₄ increased by 8.3 ± 0.6 ppb. CH₄ mole fractions averaged over polar northern latitudes and the Southern Hemisphere increased more than other zonally averaged regions. In 2008, globally averaged CH₄ increased by 4.4 ± 0.6 ppb; the largest increase was in the tropics, while polar northern latitudes did not increase. Satellite and in situ CO observations suggest only a minor contribution to increased CH₄ from biomass burning. The most likely drivers of the CH₄ anomalies observed during 2007 and 2008 are anomalously high temperatures in the Arctic and greater than average precipitation in the tropics. Near-zero CH₄ growth in the Arctic during 2008 suggests we have not yet activated strong climate feedbacks from permafrost and CH₄ hydrates.

Dlugokencky, E. J., et al. (2009), Observational constraints on recent increases in the atmospheric CH₄ burden, Geophys. Res. Lett., 36, L18803; doi: 10.1029/2009GL039780.

Link to abstract:  http://www.agu.org/pubs/crossref/2009/2009GL039780.shtml

Joseph Romm: NOAA paper smacks down the deniers - surface temperatures in the U.S. have risen rapidly in last 50 years, no doubt about it

From Climate Progress blog by Joseph Romm

Must-read NOAA paper smacks down the deniers: Q: “Is there any question that surface temperatures in the United States have been rising rapidly during the last 50 years?” A: “None at all.”

July 7th, 2009

Nothing occupies global warming deniers more than trying to prove the U.S. temperature record — a tiny portion of the global temperature record — is not reliable. Now NOAA’s National Climatic Data Center has issued an excellent Q&A, “Is the U.S. Temperature Record Reliable?” that should settle that question for any objective observer.

The NCDC paper proves we should all be delighted that deniers like Anthony Watts and Steve McIntyre spend so much time on this: It is clearly a fruitless effort that consumes time which they might otherwise spend spinning out more potent disinformation.

Consider this definitive NCDC graph comparing the U.S. temperature record since 1950 “using 1221 stations in NOAA’s Historical Climatology Network (USHCN)” [red line] with “the 70 stations that surfacestations.org classified as good or best” [purple line].

No discernible difference!

Imagine all the effort by Watts and his cohorts at surfacestations.org and WattsUpWithThat have expended examining some 70% of the 1221 stations around the country — and all they ended up proving is that the best stations give the exact same output as all the rest of the stations!

NCDC explains exactly what this chart means:

We would expect some differences simply due to the different area covered: The 70 stations only covered 43% of the country with no stations in, for example, New Mexico, Kansas, Nebraska, Iowa, Illinois, Ohio, West Virginia, Kentucky, Tennessee or North Carolina. Yet the two time series, shown [above] as both annual data and smooth data, are remarkably similar. Clearly there is no indication from this analysis that poor station exposure has imparted a bias in the U.S. temperature trends.

And this result matches a previous analysis:

Q. How has the poor exposure biased local temperatures trends?

A. At the present time (June 2009), to the best of our knowledge, there has only been one published peer-reviewed study that specifically quantified the potential bias in trends caused by poor station exposure (Peterson, 2006). The analysis examined only a small subset of stations –- all that had their exposure checked at that time -– and found no bias in long-term trends.

But what about all of those photos Watts et al. have assembled of temperature stations in dubious locations?

Q. Does a station with good exposure read warmer than a station with poor exposure?

A. Not necessarily. Many local factors influence the observed temperature: whether a station is in a valley with cold air drainage, whether the station is a liquid-in-glass thermometer in a standard wooden shelter or an electronic thermometer in the new smaller and more open plastic shelters, whether the station reads and resets its maximum and minimum thermometers in the coolest time of the day in early morning or in the warmest time of the day in the afternoon, etc. But for detecting climate change, the concern is not the absolute temperature – whether a station is reading warmer or cooler than a nearby station over grass – but how that temperature changes over time.

And so in spite of the best efforts of the deniers, the American public should have every confidence in the U.S. temperature record and the rather painfully obvious conclusion that the planet is warming:

Q. Is there any question that surface temperatures in the United States have been rising rapidly during the last 50 years?

A. None at all. Even if NOAA did not have weather-observing stations across the length and breadth of the United States the impacts of the warming are unmistakable. For example, lake and river ice is melting earlier in the spring and forming later in the fall. Plants are blooming earlier in the spring. Mountain glaciers are melting. Coastal temperatures are rising. And a multitude of species of birds, fish, mammals and plants are extending their ranges northward and, in mountainous areas, upward as well.

Some may question whether the climate scientists at NCDC should spend time on this sort of report. I think it is good to take the deniers on when it can be done in a simple, straightforward manner. I think the entire report is worth reading. It is a model of how real climatologists work.

As a model of how real climatologists don’t work, you can turn to the response to the NCDC paper that Watts published on WattsUpWithThat. For reasons that should baffle everyone, he gives the task to Roger Pielke, Sr. I’m not going to waste time rebutting his rebuttal. It is so unserious that Pielke doesn’t even reprint the devastating figure above. Then again, Pielke is a “climatologist” who thinks that the long-term 30-year trend in shrinking Arctic ice is somehow refuted by data “since 2008” (see “Roger Pielke, Sr., also doesn’t understand the science of global warming — or just chooses to willfully misrepresent it”).

Kudos to NCDC for this analysis.

Related posts:

• Must read from Hansen: Stop the madness about the tiny revision in NASA’s temperature data!
• Exclusive: New NSIDC director Serreze explains the “death spiral” of Arctic ice, brushes off the “breathtaking ignorance” of blogs like WattsUpWithThat

Link to Climate Progress blog: http://climateprogress.org/2009/07/07/noaa-ncdc-is-the-us-temperature-record-reliable-deniers-anthony-watts-surfacestationsorg/

Advanced Along-Track Scanning Radiometer (AATSR): 2007 sea-surface temperature anomalies

Remote control

12 May 2008

Soaring sea-surface temperatures led to unprecedented sea-ice loss during the 2007 Arctic summer. Without satellites, say David Llewellyn-Jones and Matt Pritchard, we may never have known the true extent of the loss.

During the summer of 2007, Gary Corlett, a scientist from the University of Leicester who is responsible for validating sea-surface temperature satellite data, received an unusual phone call. The caller said the satellite Gary monitored was throwing out exceptionally high sea-surface temperature data in the Arctic. He wanted to know if the satellite instrument was malfunctioning.

It wasn't. The Advanced Along-Track Scanning Radiometer (AATSR) was working perfectly, as it and its predecessors have done for 16 years. What it was measuring, though, was highly unusual. Sea-surface temperatures between the Bering Straits and the North Pole had rocketed to between 8 and 10°C above expectation -- by any standards a huge and unprecedented anomaly, and a major environmental event. That summer, extraordinary amounts of Arctic sea ice disappeared from the region.

Scientists blamed the unusually cloud-free conditions in the early summer, resulting in non-stop solar heating of the ocean surface. But, we're still waiting for a full explanation of the underlying reasons. The major scientific question is: 'was this a fluctuation in local behaviour or was this a manifestation of climate change?' This year's observations will attract huge interest.

Sea-surface temperatures rocketed 8-10ºC above expectation.

This is a striking example of how a satellite instrument can add to our knowledge of natural processes on a global scale. The AATSR instrument is the third in a series of similar instruments originally proposed and designed by the UK research community and largely funded by NERC in the early stages. It is now the gold standard for sea-surface temperature measurements and has spawned several hundred articles in scientific journals.

The instrument is not limited to ocean temperatures. It can also measure land-surface temperature. In contrast to the oceans, land-surface temperatures are highly diverse and complex, sparking off fundamental questions about what we actually mean by the temperature of, say, a forest, a cabbage field or a desert surface. There are some serious scientific challenges in interpreting land-surface temperature data, but we can already appreciate its power. Take, for example, land-surface temperatures over the UK during the heat-waves of summer 2006. The AATSR data not only show unusually high temperatures but, more importantly, they also reveal changing temperature patterns and gradients over time. Such information is important to meteorologists because the land surface is the bottom boundary of the atmosphere and so can help forecast systems, as well as aiding hydrologists examining the relationships between heating rates and amounts of ground moisture.

The story of the AATSR goes back to the late 1970s, when UK climate researchers called for accurate, systematic global measurements of sea-surface temperatures. Only monitoring from space would offer the required coverage, consistency and continuity.

The research community were setting satellite engineers a difficult problem: could a space-borne remote-sensing instrument achieve the required accuracy? Laboratory radiometers _ instruments that can measure the heat radiating from an object _ could certainly achieve this, but doing it from space with an intervening atmosphere? After some deep thought, a UK consortium of atmospheric scientists and space engineers, proposed the Along-Track Scanning Radiometer (ATSR) which used a novel two-angle view of the Earth's surface to correct for atmospheric effects. By observing each point on the Earth's surface via two different paths through the atmosphere, researchers can remove the effect of the atmosphere on the measurements.

Massive sea-ice loss during the 2007 Arctic summer. Map shows sea ice extent September 2007 (left) and September 2005 (right). The pink coloured line marks the median ice edge.

The European Space Agency accepted the UK's proposal and launched the instrument on board its European Remote Sensing (ERS-1) satellite in 1991. Its success led to a second instrument, ATSR-2, which was launched on the ERS-2 satellite in 1995.
Later, with ATSR-2 proving its worth, the Department for Environment, Food and Rural Affairs (Defra) funded a third instrument on board the European Space Agency's Envisat satellite, the largest Earth observation satellite ever built. The third sensor _ the Advanced ATSR _ marked the evolution from research instrument to operational observing system with real-life applications.

The latest validation results demonstrate that AATSR can measure global sea-surface temperature to about two-tenths of a degree - to measure the temperature of your own bath water to that accuracy would be quite impressive! This level of accuracy is required for climate research because rates of heat transfer between oceans and the atmosphere, which influence our weather and climate, are particularly sensitive to small changes in water temperature, especially in the tropics. Also, the changes in sea-surface temperature which may indicate global change are typically a few tenths of a degree per decade, requiring great precision and stability in any measuring system.

Recently, we've seen a step-change in the exploitation of AATSR data when the Met Office introduced sea-surface temperature data into its weather forecasting system. This major development is not only due to the AATSR's accuracy and reliability, but also down to efforts to provide the data in the form operational users like the Met Office need.
The AATSR is a key player in a pilot scheme (www.ghrsst-pp.org) to provide sea-surface temperature data, from many sensors not just satellites, to more operational users like the Met Office. The service is tailored to individual users' particular requirements. In some cases, scientists are using the AATSR instrument to verify the accuracy of measurements taken by buoys on the ocean surface: a truly remarkable achievement for an instrument 800km above the Earth.

---

Professor David T Llewellyn-Jones, based at the Space Research Centre at the University of Leicester, is the principal investigator for the AATSR Programme.

Dr Matt Pritchard is development manager at the NERC Earth Observation Data Centre, Rutherford Appleton Laboratory.

NERC with Australian partners funded the first two instruments: ATSR-1 and ATSR-2. The AATSR instrument is funded by the UK Department for Environment, Food and Rural Affairs (Defra), with significant contributions from Australia.

One of five new satellites proposed as part of the European Commission and European Space Agency's Global Monitoring for Environment and Security (GMES) programme (see Sat-nav in this issue) will carry an ATSR-style instrument. This will maintain the continuity of the ATSR dataset.

Link to article: http://planetearth.nerc.ac.uk/features/story.aspx?id=95

E. Zorita et al., How unusual is the recent series of warm years?

Citation: Zorita, E., T. F. Stocker, and H. von Storch (2008), How unusual is the recent series of warm years?, Geophys. Res. Lett., 35, L24706, doi:10.1029/2008GL036228.

How unusual is the recent series of warm years?

How unusual is the recent series of warm years?

E. Zorita (Institute for Coastal Research, GKSS Research Centre, Geesthacht, Germany), T. F. Stocker (Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland) and H. von Storch (Institute for Coastal Research, GKSS Research Centre, Geesthacht, Germany)

Previous statistical detection methods based partially on climate model simulations indicate that, globally, the observed warming lies very probably outside the natural variations. We use a more simple approach to assess recent warming at different spatial scales without making explicit use of climate simulations. It considers the likelihood that the observed recent clustering of warm record-breaking mean temperatures at global, regional and local scales may occur by chance in a stationary climate. Under two statistical null-hypotheses, autoregressive and long-memory, this probability turns to be very low: for the global records lower than p = 0.001, and even lower for some regional records. The picture for the individual long station records is not as clear, as the number of recent record years is not as large as for the spatially averaged temperatures.

(Received 5 October 2008, accepted 18 November 2008, published 30 December 2008.)

Link to abstract: http://www.agu.org/pubs/crossref/2008/2008GL036228.shtml

Eduardo Zorita: 13 of last 17 years hottest since 1880 statistically unlikely in stable climate

Glut of hot years a coincidence? Fat chance

by Catherine Brahic, NewScientist, December 17, 2008

Thirteen of the hottest years since records of global temperatures began in 1880 have clustered in the last 17 years. It is tempting – and it sure makes good headlines – to blame it on climate change. But does science support such a claim?

According to new statistical research, it does. The recent glut of unusually hot years is incredibly unlikely to happen in a stable climate.

Eduardo Zorita of Germany's Institute for Coastal Research and colleagues calculated the probability of this happening in a range of scenarios.

A key consideration is that the weather one year is not independent of the weather the year before. If it were, the odds of having any given temperature would be the same each year, and the likelihood of getting a such a 17-year cluster would be tiny – on the order of 1 in 10 trillion.

Natural memory

"An anomalous warm year tends to be followed by a warm year," says Zorita, because of the way oceans store heat and release it slowly. "A devil's advocate could argue that the clustering of warmest years at the end of the record could be simply due to chance, since the climate system has a natural memory."

However, even when Zorita included this natural feedback in his model, but excluded global warming, the odds of observing the cluster of record-breaking years was still about 1 in 10,000.

"We cannot ascribe the anomaly to any particular physical factor, like anthropogenic greenhouse gases," says Zorita. "But our conclusions are consistent with those of the fourth IPCC report," which states there is a very high probability that human emissions are causing global warming.

Journal reference (not yet accessible): Geophysical Research Letters (DOI: 10.1029/2008GL036228, in press).

Link to article: http://www.newscientist.com/article/dn16292-glut-of-hot-years-a-coincidence-fat-chance.html

NASA's GISS-TEMP Surface Temperature Analysis Methodology and Modifications' History

Link to NASA: http://data.giss.nasa.gov/gistemp/

GISS Surface Temperature Analysis

Graphs
Global Maps
Station Data
Animations
Time Series of Zonal Means
Seasonal Cycle of Zonal Means

Latest News

2008-06-07: Various insignificant changes to analysis, see "Updates to Analysis" below.

2008-03-01: USHCN data now taken from NOAA's ftp site rather than from CDIAC website. For more, see "Updates to Analysis" below.

2008-01-16: The 2007 temperature summation has been posted. There is also a related NASA news release.

History

The basic GISS temperature analysis scheme was defined in the late 1970s by James Hansen when a method of estimating global temperature change was needed for comparison with one-dimensional global climate models. Prior temperature analyses, most notably those of Murray Mitchell, covered only 20-90°N latitudes. Our rationale was that the number of Southern Hemisphere stations was sufficient for a meaningful estimate of global temperature change, because temperature anomalies and trends are highly correlated over substantial geographical distances. Our first published results (Hansen et al. 1981) showed that, contrary to impressions from northern latitudes, global cooling after 1940 was small, and there was net global warming of about 0.4°C between the 1880s and 1970s.

The analysis method was documented in Hansen and Lebedeff (1987), showing that the correlation of temperature change was reasonably strong for stations separated by up to 1200 km, especially at middle and high latitudes. They obtained quantitative estimates of the error in annual and 5-year mean temperature change by sampling at station locations a spatially complete data set of a long run of a global climate model, which was shown to have realistic spatial and temporal variability.

This derived error bar only addressed the error due to incomplete spatial coverage of measurements. As there are other potential sources of error, such as urban warming near meteorological stations, etc., many other methods have been used to verify the approximate magnitude of inferred global warming. These methods include inference of surface temperature change from vertical temperature profiles in the ground (bore holes) at many sites around the world, rate of glacier retreat at many locations, and studies by several groups of the effect of urban and other local human influences on the global temperature record. All of these yield consistent estimates of the approximate magnitude of global warming, which has now increased to about twice the magnitude that we reported in 1981. Still further affirmation of the reality of the warming is its spatial distribution, which shows largest values at locations remote from any local human influence, with a global pattern consistent with that expected for response to global climate forcings (larger in the Northern Hemisphere than the Southern Hemisphere, larger at high latitudes than low latitudes, larger over land than over ocean).

Some improvements in the analysis were made several years ago (Hansen et al. 1999; Hansen et al. 2001), including use of satellite-observed night lights to determine which stations in the United States are located in urban and peri-urban areas, the long-term trends of those stations being adjusted to agree with long-term trends of nearby rural stations.

Current Analysis Method

The current analysis uses surface air temperatures measurements from the following data sets: the unadjusted data of the Global Historical Climatology Network (Peterson and Vose, 1997 and 1998), United States Historical Climatology Network (USHCN) records through 2005, and SCAR (Scientific Committee on Antarctic Research) data from Antarctic stations. The basic analysis method is described by Hansen et al. (1999), with several modifications described by Hansen et al. (2001) also included. The GISS analysis is updated monthly.

The GHCN/USHCN/SCAR data are modified in two steps to obtain station data from which our tables, graphs, and maps are constructed. In step 1, if there are multiple records at a given location, these are combined into one record; in step 2, the urban and peri-urban (i.e., other than rural) stations are adjusted so that their long-term trend matches that of the mean of neighboring rural stations. Urban stations without nearby rural stations are dropped.

A global temperature index, as described by Hansen et al. (1996), is obtained by combining the meteorological station measurements with sea surface temperatures based in early years on ship measurements and in recent decades on satellite measurements. Uses of this data should credit the original sources, specifically the British HadISST group (Rayner and others) and the NOAA satellite analysis group (Reynolds, Smith and others). (See references.)

The analysis is limited to the period since 1880 because of poor spatial coverage of stations and decreasing data quality prior to that time. Meteorological station data provide a useful indication of temperature change in the Northern Hemisphere extratropics for a few decades prior to 1880, and there are a small number of station record s that extend back to previous centuries. However, we believe that analyses for these earlier years need to be carried out on a station by station basis with an attempt to discern the method and reliability of measurements at each station, a task beyond the scope of our analysis. Global studies of still earlier times depend upon incorporation of proxy measures of temperature change. References to such studies are provided in Hansen et al. (1999).

Programs used in the GISTEMP analysis and documentation on their use are available for download. The programs assume a Unix-like operating system and require familiarity with FORTRAN, C and Python for installation.

Updates to Analysis

Graphs and tables are updated around the 10th of every month using the current GHCN and SCAR files. The new files incorporate reports for the previous month and late reports and corrections for earlier months. NOAA updates the USHCN data at a slower, less regular frequency. We will switch to a later version, as soon as a new complete year is available.

Several minor updates to the analysis have been made since its last published description by Hansen et al. (2001). After a testing period they were incorporated at the time of the next routine update. The only change having a detectable influence on analyzed temperature was the 7 August 2007 change to correct a discontinuity in 2000 at many stations in the United States. This flaw affected temperatures in 2000 and later years by ~0.15°C averaged over the United States and ~0.003°C on global average. Contrary to reports in the media, this minor flaw did not alter the years of record temperature, as shown by comparison here of results with the data flaw ('old analysis') and with the correction ('new analysis').

August 2003:A longer version of Hohenpeissenberg station data was made available to GISS and added to the GHCN record. This had no noticeable impact on the global analyses.

March 2005:SCAR data were added to the analysis. This increased data coverage over Antarctica, as evident in the global maps of temperature anomalies.

April 2006:HadISST ocean temperatures are now used only for regions that are identified as ice-free in both the NOAA and HadISST records. This change effects a small number of gridboxes in which HadISST has sea ice while NOAA has open water. The prior approach damped temperature change at these gridboxes because of specification of a fixed temperature in sea ice regions. The new approach still yields a conservative estimate of surface air temperature change, as surface air temperature usually changes markedly when sea ice is replaced by open water or vice versa. Because of the small area of these gridboxes the effect on global temperature change was negligible.

August 7, 2007:A discontinuity in station records in the U.S. was discovered and corrected (GHCN data for 2000 and later years were inadvertently appended to USHCN data for prior years without including the adjustments at these stations that had been defined by the NOAA National Climate Data Center). This had a small impact on the U.S. average temperature, about 0.15°C, for 2000 and later years, and a negligible effect on global temperature, as is shown here.

This August 2007 change received international attention via discussions on various blogs and repetition by some other media, with no graphs provided to show the insignificance of the effect. Further discussions of the curious misinformation are provided by Dr. Hansen on his personal webpage (e.g., his post on "The Real Deal: Usufruct & the Gorilla").

September 10, 2007: The year 2000 version of USHCN data was replaced by the current version (with data through 2005). In this newer version, NOAA removed or corrected a number of station records before year 2000. Since these changes included most of the records that failed our quality control checks, we no longer remove any USHCN records. The effect of station removal on analyzed global temperature is very small, as shown by graphs and maps available here.

March 1, 2008: Starting with our next update, USHCN data will be taken from NOAA's ftp site -- the original source for that file -- rather than from CDIAC's web site; this way we get the most recent publicly available version. Whereas CDIAC's copy currently ends in 12/2005, NOAA's file extends through 5/2007. Note: New updates usually also include changes to data from previous years. Whereas the GHCN and SCAR data are updated every month, updates to the USHCN data occur at irregular intervals.

The publicly available source codes were modified to automatically adjust if new years are added.

June 9, 2008: Effective June 9, 2008, our analysis moved from a 15-year-old machine (soon to be decommissioned) to a newer machine; this will affect some results, though insignificantly. Some sorting routines were modified to minimize such machine dependence in the future. In addition, a typo was discovered and corrected in the program that dealt with a potential discontinuity in the Lihue station record. Finally, some errors were noticed on http://www.antarctica.ac.uk/met/READER/temperature.html (set of stations not included in Met READER) that were not present before 8/2007. We replaced those outliers with the originally reported values. Those two changes had about the same impact on the results than switching machines (in each case the 1880-2007 change was affected by 0.002°C). See graph and maps.

Annual Summations

NASA news releases about the GISS surface temperature analysis are available for 2007, 2006, 2005, and 2004.

We also provide here more detailed discussions of global surface temperature trends for 2007, 2005, 2004, 2003, 2002, and 2001.

Table Data: Global and Zonal Mean Anomalies dT_s

Plain text files in tabular format of temperature anomalies. Anomaly values indicate the difference from the corresponding 1951-1980 means.

• Global-mean monthly, annual and seasonal dT_s based on met.station data, 1880-present, updated through most recent month
• Northern Hemisphere-mean monthly, annual and seasonal dT_s based on met.station data, 1880-present, updated through most recent month
• Southern Hemisphere-mean monthly, annual and seasonal dT_s based on met.station data, 1880-present, updated through most recent month
• Global-mean monthly, annual and seasonal land-ocean temperature index, 1880-present, updated through most recent month
• Zonal-mean annual dT_s, 1880-present, updated through most recent complete calendar year
• Zonal-mean annual land-ocean temperature index, 1880-present, updated through most recent completed year

Gridded Monthly Maps of Temperature Anomaly Data

Users interested in the entire gridded temperature anomaly data may download the three basic binary files from our ftp site. Also available there are various FORTRAN programs and instructions to create (time series of) regular gridded anomaly maps from these files. This should make the maintenance of the files mentioned below unnecessary.

Data files for individual years may be obtained from the ftp site's subdirectories: bin for binary format, txt for ASCII text, and netcdf for netCDF.
These files will no longer be updated; they will eventually be removed from this site.

Anomalies and Absolute Temperatures

Our analysis concerns only temperature anomalies, not absolute temperature. Temperature anomalies are computed relative to the base period 1951-1980. The reason to work with anomalies, rather than absolute temperature is that absolute temperature varies markedly in short distances, while monthly or annual temperature anomalies are representative of a much larger region. Indeed, we have shown (Hansen and Lebedeff, 1987) that temperature anomalies are strongly correlated out to distances of the order of 1000 km. For a more detailed discussion, see The Elusive Absolute Surface Air Temperature.

References

Please see the GISTEMP references page for more citations to publications related to this research.

Copies of many of our papers are available in the GISS publications database. Re-prints not available there may be obtained by request from Dr. James Hansen.

Contacts

Please address scientific inquiries about the GISTEMP analysis to Dr. James Hansen.

Please address technical questions about these GISTEMP webpages to Dr. Reto Ruedy.

Also participating in the GISTEMP analysis are Dr. Makiko Sato and Dr. Ken Lo.

NASA: The Earth's Temperature Tracker

BLOGGER'S NOTE: I have a terrible memory, and I often want to refresh it with regard to the way in which GISS temperatures are calculated. The Earth Observatory website is often inaccessible, so I have saved this article, here, for my own benefit and the benefit of anyone who is interested. Many denialists take potshots at the methodology, accusing the scientists of intentional manipulation of the data to show warmer temperatures. Note that GISS-TEMP extrapolates to include the Poles and other temperature trackers do not. As the Arctic is warming faster than any other region of the planet, this would naturally mean that GISS-TEMPs will be slightly warmer, but they still show no significant difference in the trend when compared to the other temperature trackers. Occasionally, GISS-TEMPs are even lower than the other trackers. The second link below is to a more lengthy discussion of the history of the methodology and the changes that have been made over the years. Due to the fact that occasionally I cannot open the NASA page, I will also post the contents of that page, later.

Link to NASA's Earth Observatory:
http://earthobservatory.nasa.gov/Study/GISSTemperature/giss_temperature.html

Link to GISS temperature analysis and history: http://data.giss.nasa.gov/gistemp/

	by David Herring • design by Robert Simmon • November 5, 2007
	Gazing up at the patch of night sky where the moon had shone just minutes earlier, young James Hansen had a flash of insight that changed the course of his career. It was December 1963 and Hansen, a senior in college, had gathered with fellow students at a small observatory just outside of Iowa City to observe a lunar eclipse. As the moon entered Earth’s shadow, Hansen expected the lunar disk to grow dark but he didn’t expect it to completely disappear from view. At first the moon’s disappearance puzzled Hansen, but then it dawned on him that it must have something to do with the recent eruption of Mount Agung, in Indonesia. Agung Volcano erupted with such force on March 17, 1963, it injected gases and debris particles high into the atmosphere, above where rain clouds form. Over a span of weeks the volcanic particles spread around the upper atmosphere where they scattered and absorbed incoming light, slightly darkening Earth’s surface.
	“Normally you can see the moon during an eclipse from the sunlight beams refracted into Earth’s shadow,” Hansen explained, referring to the way in which the atmosphere bends light beams. “But on that night the atmosphere was so filled with volcanic aerosols that the sunlight beams that usually bent into the moon’s shadow region couldn’t penetrate Earth’s atmosphere well. So it appeared to us as a remarkably dark eclipse.”		During a lunar eclipse the moon usually remains visible, dimly lit by sunlight refracted through Earth’s atmosphere. In December of 1963, however, particles in the atmosphere from the eruption of Mount Agung blocked enough sunlight to make the eclipsed moon almost invisible. (Photograph ©2007 Johannes Schedler.)
	Hansen marveled at the power of these airborne particles, known as aerosols. If aerosols can reflect and absorb incoming sunlight, what effect could events like Agung's eruption have on Earth’s surface temperature? To find out, he plugged what was known at the time about aerosols, greenhouse gases, and how Earth absorbs and radiates energy into some physics equations. His results suggested that the aerosols should slightly cool the planet. It was one thing to estimate the impact of volcanic eruptions on global temperature using math and physics. It was quite another thing to compare such estimates to real-world data. The problem was that there were no real-world, global-scale data sets of temperature in the late 1960s to which he could compare his estimates. Murray Mitchell, in the NOAA Weather Bureau’s Office of Climatology, collected the most complete data set at the time. But Mitchell’s data set only included stations in the Northern Hemisphere. Thus Hansen’s goal of comparing his estimates to the real world was put on hold.		A catastrophic eruption of Mount Agung in March 1963 killed over 1,000 Indonesians on the island of Bali. The eruption covered the nearby area with ash and injected sulfur compounds into the stratosphere. The particles remained aloft for several years, absorbing and scattering light and slightly cooling Earth’s surface. (Photograph ©2006 Jesse Wagstaff.)
	He continued working on planetary-scale science problems throughout his graduate and post-graduate studies. The United States had become a space-faring nation and the allure of the unknown called many planetary physicists’ attention to worlds beyond Earth’s atmosphere. What were conditions on the other planets like, and could they support life as we know it? Hansen wrote his doctoral thesis on the atmosphere of Earth’s nearest neighbor, Venus. Its dense carbon dioxide atmosphere made Venus’ surface hotter than an oven. Years later Hansen’s studies of Venus would contribute to his efforts to track Earth’s temperature.		The dense carbon dioxide atmosphere of Venus shrouds the planet in a thick layer of clouds—and heats the surface to a scorching 460° C (860° F). Jim Hansen’s research on Venus’ greenhouse effect eventually led him to the study of carbon dioxide and the greenhouse effect on Earth. (Image ©2005 Mattias Malmer.)

	Earth is Cooling…No It’s Warming
	In 1967 Hansen went to work for NASA’s Goddard Institute for Space Studies, in New York City, where he continued his research on planetary problems. Around 1970, some scientists suspected Earth was entering a period of global cooling. Decades prior, the brilliant Serbian mathematician Milutin Milankovitch had explained how our world warms and cools on roughly 100,000-year cycles due to its slowly changing position relative to the Sun. Milankovitch’s theory suggested Earth should be just beginning to head into its next ice age cycle. The surface temperature data gathered by Mitchell seemed to agree; the record showed that Earth experienced a period of cooling (by about 0.3°C) from 1940 through 1970. Of course, Mitchell was only collecting data over a fraction of the Northern Hemisphere—from 20 to 90 degrees North latitude. Still, the result drew public attention and a number of speculative articles about Earth’s coming ice age appeared in newspapers and magazines.
	But other scientists forecasted global warming. Russian climatologist Mikhail Budyko had also observed the three-decade cooling trend. Nevertheless, he published a paper in 1967 in which he predicted the cooling would soon switch to warming due to rising human emissions of carbon dioxide. Budyko’s paper and another paper published in 1975 by Veerabhadran Ramanathan caught Hansen’s attention. Ramanathan pointed out that human-made chlorofluorocarbons (or CFCs) are particularly potent greenhouse gases, with as much as 200 times the heat-retaining capacity of carbon dioxide. Because people were adding CFCs to the lower atmosphere at an increasing rate, Ramanathan expressed concern that these new gases would eventually add to Earth’s greenhouse effect and cause our world to warm. (Because CFCs also erode Earth’s protective ozone layer, their use was mostly abolished in 1989 with the signing of the Montreal Protocol.) The notion that humans could override nature and force the globe to warm intrigued Hansen. “It had been known for more than a century that increasing carbon dioxide could have an effect on global temperature,” Hansen said (referring to the pioneering work of John Tyndall and Svante Arrhenius in the 1800s). But global warming in the near future? That was another matter. Hansen returned his attention to the physics equations he’d played with almost 10 years earlier. Collaborating with Andy Lacis, a colleague at NASA, he built a simple climate model to simulate how changes in the atmosphere cause Earth’s average temperature to change over time. Hansen and Lacis tweaked the inputs to simulate the cumulative influence of all known human-made greenhouse gases except carbon dioxide (including CFCs, methane, nitrous oxide, and ozone) to see if their net effect could even be felt on a global scale in the climate system. To their surprise, Hansen’s team found that the warming effect of all those gases added together is comparable to the warming effect of carbon dioxide alone.		Initial efforts to observe Earth’s temperature were limited to the Northern Hemisphere, and they showed a cooling trend from 1940 to 1970 (jagged line). Scientists estimated the relative effects of carbon dioxide (warming, top curve) and aerosols (cooling, bottom curve) on climate, but did not have enough data to make precise predictions. (Graph from Mitchell, 1972.)
	The simple model also allowed Hansen to simulate the climate impact of Mount Agung’s eruption 15 years after the event. The model indicated that loading the atmosphere with volcanic aerosols should have caused a global cooling—a prediction that agreed pretty well with observed temperature data. The model demonstrated that both human and natural activities could force climate to change. But Hansen knew that natural forcings, like volcanic eruptions or changes in the Sun’s activity, tend to go up and down over a long period of time whereas the human forcing from greenhouse gas emissions was steadily increasing. “It became clear that human-produced greenhouse gases should become a dominant forcing and even exceed other climate forcings, such as volcanoes or the Sun, at some point in the future,” Hansen observed. How soon would the human forcing begin to dominate? No one knew.		In 1981, NASA scientists predicted the impact of carbon dioxide emissions on global temperatures between 1950 and 2100 based on different scenarios for energy growth rates and energy source. If energy use stayed constant at 1980 levels (scenario 3, bottom lines), temperatures were predicted to rise just over 1°C. If energy use grew moderately (scenario 2, middle lines), warming would be 1–2.5 °C. Fast growth (scenario 1, top lines) would cause 3–4°C of warming. In each scenario, the warming was predicted to be less if some of the energy was supplied by non-fossil (renewable) fuels instead of coal-based, synthetic fuels (synfuels). (Graph from Hansen et al., 1981.)
	To find out, Hansen would need real-world data on a global scale. He requested data tapes from Roy Jenne, of the National Center for Atmospheric Research, who was widely recognized in the 1970s as having the best weather dataset in the world. Of course, there remained the problem that the weather stations supplying Jenne’s dataset were rather sparse compared to the vastness of Earth’s surface.		To test his climate model, Hansen calculated the cooling effect of Mount Agung’s eruption (dotted line) and compared the results with real-world temperature measurements (solid line). Despite its simplicity, the model accurately reflected the dip in tropical temperatures caused by the eruption. (Graph from Hansen et al., 1978.)
	“The lack of any global temperature analysis [for Earth] did not seem right to me,” Hansen recalled. Drawing from his previous work in estimating the average planetary surface temperature of Venus, he knew that if scientists had measurements from as many places on another planet as were available from Jenne’s dataset they would not hesitate to estimate Earth’s global temperature. He decided to try. At the outset Hansen knew that weather fluctuations would introduce short-term temperature anomalies into the weather station dataset that are not the same thing as climate change. But he reasoned that by taking averages over several years, and appropriately “weighting” the weather stations’ data, it should be possible to determine meaningful temperature changes over longer time periods. In the mid-1970s, he hired Jeremy Barberra, a New York University undergraduate student at the time, to automate the processing of Jenne’s dataset. They decided to process the data to produce average temperature changes, and not absolute temperature. “If you focus your analysis on temperature change, and not on determining absolute temperature values, then the station coverage is adequate,” Hansen explained. “What matters is the long-term mean over large scales, not single measurements from individual stations.” The success of Hansen’s and Barberra’s approach depended on the principle that temperature anomalies have a much larger scale than absolute temperature. Consider a mountain on which it can be much cooler on one side than the other. This example illustrates how absolute temperature patterns can vary sharply over relatively short distances. On the other hand, temperature anomalies are typically large-scale events driven by Rossby Waves. Rossby Waves are slow-moving waves in the ocean or atmosphere, driven from west to east by the force of Earth spinning. We see such waves in the atmosphere as large-scale meanders of the mid-latitude jet stream.		Weather stations (red dots) are scattered unevenly across the globe. They are especially sparse in Africa and over the oceans. Before scientists could be confident in global temperature records, Hansen needed to demonstrate that widely spaced observations captured global temperature trends accurately. (NASA map by Robert Simmon, based on data from the National Climatic Data Center.)
	“If it is an unusually warm winter in New York, it is probably also warm in Washington, D.C., for example,” Hansen explained. “At high- and mid-latitudes Rossby Waves are the dominant cause of short-term temperature variations. And since those are fairly long waves we didn’t think we needed a station at every one degree of separation.” A station at every 1 degree would mean a station roughly every 80 kilometers (at mid-latitudes). But in a 1987 paper appearing in the Journal of Geophysical Review, Hansen and Sergei Lebedeff demonstrated that the temperature readings of weather stations within 1,000 kilometers (620 miles) of one another are highly correlated. The close correlation meant they could map global temperature changes over time despite the fact that weather stations are widely spaced and located mainly on continents and islands. Here’s basically how their approach works: For each center point in a global grid of 1-degree boxes they let all weather station data within a 1,200-kilometer radius influence the estimated temperature change at that point. They gave greatest “weight” to the station closest to that point; for all other stations within that radius, they let the weighting fall off linearly with distance, all the way to a weighting of zero for stations 1,200 kilometers away or farther. “Again, our objective was not to determine the precise temperature of individual stations, but to produce a global-scale map of temperature change,” Hansen emphasized. “We were interested in tracking global climate patterns, not local weather variations.” In their 1981 analysis, published in the journal Science, Hansen’s team reported finding that, overall, Earth’s average temperature rose by about 0.4°C for the period from 1880 to 1978. There was roughly 0.1°C of global cooling from 1940-1970. This cooling was less than what Mitchell had found earlier due to the fact that Hansen’s team was now using global data, and not just data from a swath around the Northern Hemisphere. Just as Budyko had predicted, Hansen found that Earth’s cooling trend swung back in the warming direction around 1970 and has been warming ever since. Moreover, Hansen noted, the warming trend observed in real-world data is consistent with his (and others’) global climate model outputs in their 100-year simulations.		Absolute temperatures can vary a lot even over short distances, but temperature anomalies usually affect a large region. Most week-to-week temperature variability is driven by Rossby Waves. These waves are easy to see in the looping motions of the jet stream. In this animation, Rossby Waves spiral from left to right toward Europe in the Northern Hemisphere and South Africa in the Southern Hemisphere. The scale of these waves is so large that weather stations separated by 1,000 kilometers or more adequately record the temperature anomalies they produce. (Double-click to pause or replay animation.) (NASA animation by Robert Simmon, based on SEVIRI data copyright EUMETSAT.) High definition animation (23 MB Quicktime)
	Since 1978, global warming has become even more apparent. Over the last 30 years, Hansen’s analysis reveals that Earth warmed another 0.5°C, for a total warming of 0.9°C since 1880.		The first reliable global measurements of temperature from NASA, published by Hansen and his colleagues in 1981, showed a modest warming from 1880 to 1980, with only a slight dip in temperatures from 1940 to 1970. (Graph adapted from Hansen et al. 1981.)
	“To questions about whether this warming is natural or just a fluctuation, the answer has become clear: the world is getting warmer,” Hansen stated. “This fact agrees so well with what we calculate with our global climate model that I am confident we are looking at warming that is mainly due to increasing human-made greenhouse gases.”		Since 1980, global surface temperatures have increased sharply, the Earth’s response to increasing concentrations of greenhouse gases such as carbon dioxide. (NASA graph adapted from Goddard Institute for Space Studies data.)

	The Data and the Details
	Some nagging questions remained for Hansen and his colleagues. Citing issues such as stations located too close to paved surfaces, stations located in urban areas that are known to be warmer than rural regions, and stations located in developing nations where data collection methods may be unreliable, critics argued that any of these problems could throw off an individual station’s temperature readings. Don’t such concerns cast a shadow of doubt on the NOAA weather station data? Initially, perhaps, but not after the data have been carefully tested in several ways. First, Hansen’s team (and others) finds good agreement of the weather station data with “proxy” data sets that are sensitive to surface temperature changes—such as the rate at which glaciers are receding, or subsurface temperature measurements in boreholes drilled down into the ground. (Scientists can infer surface temperature change from underground temperatures based on equations that describe how heat diffuses through the ground over time.) The results in thousands of remote locations around the world agree well with the surface temperature measurements. Second, Hansen’s team “cleans” the weather station data by finding and filtering out flawed data entries. Specifically, they apply a computer algorithm that checks each data point for temperature readings that are very significantly higher or lower than average for a given location at that time of year. Whenever such an anomaly is flagged, the algorithm compares those data to data from nearby stations to see if they show a similar anomaly. If so, then the data in question are kept; if not, or if there are no nearby stations for comparison, then the data are thrown away.
	His team also modifies the data from stations located in densely populated areas by removing the long-term bias of these “urban heat islands.” The team uses satellite data to determine if a given station is in an urban or near-urban location. If so, then the team uses the nearest rural stations to determine the long-term trend at the urban site. If there are no rural neighbors, then Hansen’s team throws out the urban station data.		Bad data are cleaned from the NASA global temperature record by first looking for outliers: months when the temperature at a station is much higher or lower than the average for that time of year. The monthly temperature record for Linyi, China, in 1932 (red dots; June data is missing) shows that September was 5.3° C warmer than average. The unusual data point was compared to nearby stations. Since some of those stations were also exceptionally warm, the data point was retained. If nearby stations do not confirm the anomaly, the team does not use the data. (Graph by Robert Simmon, based on data from the GISS Surface Temperature Analysis Station Data.)
	One lesson to be learned here is weather science and climate science are quite different: weather is concerned with what conditions are like at a given location and time, whereas climate is concerned with what conditions are like over large regions, or over the entire globe, and for a long period of time. That explains why climate scientists are not as interested in any given reading for an individual station as they are in 5-year and 10-year blocks of time for the entire planet. Hansen acknowledged there may be flaws in the weather station data. “But that doesn’t mean you give up on the science, and that you can’t draw valid conclusions about the nature of Earth’s temperature change,” he asserted.		Weather stations are screened for potential bias from urban heat islands by comparing station locations with maps of urbanization. Measurements from nearby stations in rural areas (gray) are used to correct urban station data for warming due to the heat island effect. If no rural neighbors are available for comparison, data from urban (dark blue) and peri-urban (blue) stations are left out of the global average calculation. (Map by Robert Simmon, based on data from NOAA.)

From A Dimmer Past to a Brighter Future?
	Of greater concern to Hansen than global warming skeptics is the problem of global warming itself. If greenhouse gases are to blame then why did Earth’s average temperature cool from 1940-1970? And why has the rate of global warming accelerated since 1978? Hansen’s answers to these questions brought him full circle to where he began his investigation more than 40 years ago. “I think the cooling that Earth experienced through the middle of the twentieth century was due in part to natural variability,” he said. “But there’s another factor made by humans which probably contributed, and could even be the dominant cause: aerosols.”
	In addition to greenhouse gas emissions, human emissions of particulate matter are another significant influence on global temperature. But whereas greenhouse gases force the climate system in the warming direction, aerosols force the system in the cooling direction because the airborne particles scatter and absorb incoming sunlight. “Both greenhouse gases and aerosols are created by burning fossil fuels,” Hansen said, “but the aerosol effect is complicated because aerosols are distributed inhomogeneously [unevenly] while greenhouse gases are almost uniformly spaced. So you can measure greenhouse gas abundance at one place, but aerosols require measurements at many places to understand their abundance.” After World War II, the industrial economies of Europe and the United States were revving up to a level of productivity the world had never seen before. To power this large-scale expansion of industry, Europeans and Americans burned an enormous quantity of fossil fuels (coal, oil, and natural gas). In addition to carbon dioxide, burning fossil fuel produces particulate matter—including soot and light-colored sulfate aerosols. Hansen suspects the relatively sudden, massive output of aerosols from industries and power plants contributed to the global cooling trend from 1940-1970.		Pollution from factories, cars, airplanes, home furnaces, and power plants form aerosols—tiny particles suspended in the air. These particles reflect and absorb sunlight, slightly cooling the Earth’s surface. (Photograph ©2007 Señor Codo.)

	“That’s my suggestion, though it’s still not proven,” he said. “There is a nice record of sulfates in Greenland ice cores that shows this type of particle was peaking in the atmosphere around 1970. And then the ice core record shows a rapid decline in sulfates, right about the time nations began regulating their emission.” (Sulfates cause acid rain and other health and environmental problems.) In 2007, Michael Mischenko, of NASA GISS, published a paper in the journal Science in which he reported tropospheric aerosols have indeed declined slightly over the last 30 years. The net effect is that more sunlight passes through the atmosphere, slightly brightening the surface. This increased exposure to sunlight could partially account for the increase in surface temperature that Mischenko and Hansen observed over the same time span.		Sulfur trapped in the Greenland Ice Sheet records the presence of reflective sulfate aerosols downwind of the United States and Canada. Emissions of the pollutants that form sulfate aerosols rose sharply in the United States and Europe during and after World War II. This rise may be responsible for the Northern Hemisphere cooling from 1940–1970. By the 1980s, oil embargos and environmental controls had reduced sulfate pollution in North America, but carbon dioxide continued to build up in the atmosphere. (Graph by Robert Simmon, based on data from McConnell et al., NOAA/NCDC Paleoclimatology Program.)
	Over the course of the twentieth century, Hansen and other climate scientists estimate aerosols may have offset global warming by as much as 50 percent by reducing the amount of sunlight reaching the surface. Scientists call this phenomenon “global dimming,” although the change was too gradual and too slight to be perceived by the human eye. (Aerosols’ dimming potential has been observed, of course, after dramatic events like the Agung Volcano eruption that Hansen noticed during the lunar eclipse of December 1963.) Hansen describes the global dimming effect of human-emitted aerosols as a “Faustian bargain”—a deal with the devil. “Eventually you get to a point where you don’t want aerosols in the atmosphere because they’re harmful to human health, harmful to agriculture, and harmful to natural resources,” he stated. “So in the U.S. and much of Europe, we’ve been reducing aerosol emissions.” But we haven’t seen a corresponding reduction in greenhouse gas emissions. Indeed, humans’ use of fossil fuels rose rapidly (about 5 percent per year) from the period after World War II until 1973. After the oil embargo and price shock of oil in 1973, annual average consumption continued to increase, but at a slower pace (between 1.5 and 2 percent per year). A byproduct of that rising fossil fuel consumption has been a corresponding rise in carbon dioxide emission. Because greenhouse gases reside in the atmosphere for decades, while aerosols usually wash out over a span of days to weeks, the warming influence of greenhouse gases gradually won out. “For much of the twentieth century, both types of human emissions were on nearly equal footing, and aerosols were able to compete with greenhouse gases,” Hansen said. But that balance has tilted increasingly in favor of greenhouse gases in the last 30 years. Today, Hansen’s team estimates the human forcing from greenhouse gases to be about 3 watts per square meter (warming) and the forcing from aerosols to be about minus 1.5 watts per square meter (cooling). Hansen sees these trends as very likely to lead to what he calls “dangerous human interference” with the climate system. “I think action [to reduce greenhouse gas emissions] is needed urgently, because we are on the precipice of a climate system ‘tipping point’,” Hansen concluded. “I believe the evidence shows with reasonable clarity that the level of additional global warming that would put us into dangerous territory is at most 1°C.”		Satellite observations of aerosol optical thickness (how greatly aerosols reduce the intensity of sunlight reaching the surface) show that aerosol concentrations have decreased since 1991 (green line). Prior to that, they had been rising slightly (blue line). In addition to the long-term trends of human-made aerosols, the graph shows the occurrence of large volcanic eruptions like El Chichón in 1982 and Mount Pinatubo in 1991. These natural events produce large spikes in aerosol concentrations, but their impact is short-lived. (Graph adapted from Mishchenko et al., 2007)
	If we follow a ‘business-as-usual’ course, Hansen predicts, then at the end of the twenty-first century we will find a planet that is 2-3°C warmer than today, which is a temperature Earth hasn’t experienced since the middle Pliocene Epoch about three million years ago, when sea level was roughly 25 meters higher than it is today.