It’s winter, and that’s the worst air pollution period for Europe and China. The levels over much of the continent are in the unhealthy range. In the figure we show a map of the pollution of particulate matter in Europe, “PM2.5”, the most lethal of the common air pollutions. The map was taken from our website: http://berkeleyearth.org/air-quality-real-time-map/ where it is updated hourly. Grey areas (such as in Italy and Russia) are regions in which hourly updates are not publicly available.

The scale of “cigarettes per day” is used to make the levels easiest to understand. They were calculated by comparing the known health risk of cigarettes to the known health risks of PM2.5 as estimated by the World Health Organization. Throughout much of Europe the pollution levels give a health effect equivalent to that of every man, woman and child smoking 5 cigarettes per day; in the worst regions of Europe, the level exceeds 7 cigarettes per day equivalent.  For more information on PM2.5 and cigarette equivalence, see our memo: http://berkeleyearth.org/air-pollution-and-cigarette-equivalence/

The second plot shows yesterday’s air pollution around the world.  The worst pollution is in India and China, where levels reach over a pack of cigarettes per day (PM2.5 above 400 micrograms per cubic meter). It was not a good day for much of the world, except for the US, Japan, and some small scattered regions. The pollution tends to be exacerbated in winter, when more fuel is burned for heat (even renewables such as wood and biomass contribute to air pollution) and when atmospheric conditions are likely to trap the pollution.

For more detailed information on Berkeley Earth’s work on air pollution, see: http://berkeleyearth.org/air-pollution-overview/.

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Air pollution in China is a critical global health problem, responsible for somewhere between 700,000 and 2.2 million premature deaths annually. The largest single contributor to air pollution-related mortality is particulate matter below 2.5 microns in size, or PM_2.5. Exposure to PM_2.5 has been found to increase the risk of heart attacks, strokes, lung disease, and lower respiratory disease based on a number of longitudinal studies around the world comparing populations with differing levels of exposure. China is the world’s largest consumer of coal, and coal is responsible for the vast majority of electricity generated in China. I’ll argue that replacing coal-based generation with low-polluting alternatives like nuclear, natural gas, or renewables could save between 200 and 1,000 lives per gigawatt-year.

China has, on average, among the worst air pollution of any country in the world. Many of the areas of the country have PM_2.5 levels that rank on average (using the U.S. EPA rating scheme) as “Unhealthy”, with spikes up to “Very Unhealthy” or “Hazardous” as relatively common occurrences. Occasionally cities in China will experience air pollution levels literally off the charts, with PM_2.5 concentrations reaching levels of 1,000 or more micrograms per cubic meter (µg/m³)—the standard health scale ends at 500 µg/m³. The image below, from Berkeley Earth’s real-time air pollution monitoring of China, shows PM_2.5 levels on a typical fall day.

Figure 1. PM2.5 levels over China and nearby countries on October 11th at 13:00 UTC. Real-time updates are available at: http://berkeleyearth.org/air-quality-real-time-map/

A large portion of PM_2.5 in China comes from coal use. When coal is burned, it both directly releases particulate matter as a result of incomplete combustion and releases sulfur dioxide, nitrogen oxides, and black carbon that serve as important precursors to atmospheric PM_2.5 formation. In many cases this secondary formation is more important than direct PM_2.5 emissions from coal. Approximately 55 percent of coal consumed in China is used to generate electricity provided to the grid. 40 percent is consumed in industrial processes, while around 3 percent is used in the residential sector (with the remaining 2 percent consumed by commercial and other). China generates approximately 3,700 terawatt-hours or 422 gigawatt-years (GW-years) of electricity from coal annually.

Determining the contribution of coal-based electricity generation to PM_2.5-related mortality requires estimating what percent of total PM_2.5 in China comes from coal-fired power plants. A recent study by the Health Effects Institute and Tsinghua University performed extensive atmospheric modeling of PM_2.5 sources to attribute overall concentrations. It found that about 40 percent of total PM_2.5 could be directly attributed to coal. Of this, about 21 percent came from industrial uses (steel production, for example), 12 percent came from power plant coal, and 6 percent came from domestic coal burning (for space heating). It is important to note, however, that they assumed widespread use of scrubbers for electric power production; if the use of scrubbers is low then a larger fraction of the observed PM_2.5 would be attributed to coal use. The breakdown of PM_2.5 by source is shown in the pie chart below, both for the high power-plant scrubber use scenario put forward by the Health Effects Institute and a low scrubber use scenario that assumes power-sector emissions are more similar to those of industry.

Figure 2. Breakdown of estimate PM_2.5 contributions by source in the Burden of Disease Attributable to Coal-Burning and Other Air Pollution Sources in China study from the Health Effects Institute and Tsinghua University in the High Scrubber Use scenario. The Low Scrubber Use scenario shows estimated contribution percentages if power plant emissions were more similar to industrial sector emissions.

The Health Effects Institute also provided an estimate of total mortality from PM_2.5 of 916,000 deaths per year (with a remarkably narrow uncertainty range of 820,000 to 993,000), a mortality that is smaller than the prior Berkeley Earth estimate of 1.6 million, although within the published uncertainty range (700,000 to 2.2 million). Assuming that 12 percent of PM_2.5 (and thus 12 percent of PM_2.5 mortality) is attributable to coal-based electricity generation, we can estimate deaths per gigawatt-year (GW-year) as 260 (uncertainty range 233 to 282) using the Heath Effects Institute numbers and 454 deaths per GW-year (uncertainty range 199 to 625) for the Berkeley Earth numbers.

It is somewhat interesting to note that while both power generation and industrial sectors consume comparable amounts of coal, the PM_2.5 contribution from the industrial sector is twice that of the electricity sector in the Heath Effects Institute model. This is largely due to assumptions regarding the utilization of emissions control technologies like sulfur, nitrogen, and PM_2.5 scrubbers. Nearly all power plants are assumed to use scrubbers, while use in the industrial sector is more spotty. However, there is strong reason to believe that the actual utilization of scrubbers by power plants is much lower than officially reported. Operation of scrubbers consumes a non-negligible amount of energy, and there have been numerous reports of plant operators shutting down scrubbers to increase profits when they can get away with it. While China has started to crack down on this behavior, it is likely that actual scrubber use is still well below the 90+ percent assumed.

We can get an estimate of the mortality contribution of coal-base electricity generation when scrubbers are not fully utilized by examining a case where coal power plants had the same PM_2.5 contribution as industry. While this is likely not true across the entire power sector, it may well be the case (or even a conservative estimate) for individual plants. This still results in effective power-sector emissions per ton of coal that are around 30 percent lower than for the industrial sector, as more coal is consumed in the power sector. If the power generation sector has emissions similar to the industrial sector, deaths per GW-year would be 456 (412 to 497) for the Heath Effects Institute mortality model and 745 (326 to 1025) for the Berkeley Earth model. These results are illustrated in Figure 3.

Figure 3. Estimated mortality per gigawatt-year of coal-based electricity generation attributable to PM_2.5. Both estimates from the Health Effects Institute and Berkeley Earth are shown. The high scrubber use scenario follows the Health Effects Institute assumptions, while the low scrubber use scenario assumes emissions allocation similar to the industrial sector. Both published error bars (solid bars) and estimated error bars using the mortality impact uncertainty in the Berkeley Earth approach (dashed bars) are shown for the Health Effects Institute numbers.

Other studies conducted on mortality impacts of coal-based generation outside of China have found comparable results. A 2007 article in The Lancet estimated a mortality rate per GW-year of 202 for the U.K. and an average value of 231 globally. These estimates are noticeably lower than most of the estimates we consider for China, though they overlap with the low end of our values. This discrepancy might be due to two factors: first, there is reason to believe that emissions control technologies are in more widespread use in places like the U.K. and other parts of the world than in China. Second, the mortality estimates will be impacted by population density, with coal generation located in coastal China (where plants are primarily concentrated) having a much larger exposure impact than in most regions of the world.

Ultimately, at 422 GW-years of electricity produced annually from coal, we can bound Chinese coal-generation-related deaths at between 84,000 (200 deaths per GW-year) and 434,000 (1025 per GW-year). The consumption of coal for electricity generation thus has large negative public health implications for China. Moving to away from coal and toward alternatives like nuclear, natural gas, and renewables such as solar and wind, would not only reduce China’s greenhouse gas emissions but also save lives.

The post Coal in China: Estimating Deaths per GW-year appeared first on Berkeley Earth.

The relationship between greenhouse gas (GHG) emissions and future warming is complex, depending on the atmospheric lifetime of gases, their radiative forcing, and the thermal inertia of the Earth, particularly our oceans. Many non-CO₂ GHGs have shorter atmospheric lifetimes, and the global warming-equivalent values commonly used for analysis of emission impacts fail to effectively capture important relationships between emission time and resulting impact on global surface temperature.

In order for researchers to easily translate emissions of CO₂, CH₄, and N₂O into future warming consistent at a global level with the results obtained from the latest generation of climate models, we have developed a simple python-based climate model we call SimMod (available on github here). If provided with annual emissions of each GHG, it will convert these into atmospheric concentrations, radiative forcing, and transient climate response (warming) per year through 2100 (or any specified period).

The model comes with four built-in emission scenarios, the IPCC’s RCP scenarios that can be used as a starting point for analysis. We also used the published atmospheric concentrations, radiative forcing, and transient climate response to evaluate the model performance. The emissions scenarios are shown in Figure 1, below.

Figure 1: Emissions of CO₂, CH₄, and N₂O for the four RCPs.

These emissions are converted into concentrations either using pulse-response functions for each gas (simple exponential decay for CH₄ and N₂O; a response function fit to the BERN carbon cycle model in the case of CO2) or using the BEAM carbon cycle model for CO₂, whichever the user specifies. The resulting atmospheric concentrations for each RCP scenario are shown in Figure 2. In general, the modeled concentrations match RCP scenarios well, with some exceptions for high CO2 emission scenarios (e.g. RCP 8.5) where carbon cycle feedbacks reduce ocean uptake in a manner not reflected in the simple pulse response model.

Figure 2: Atmospheric concentrations CO₂, CH₄, and N2O for the four RCPs and SimMod, with values normalized for the year 2000. Dashed lines are SimMod results; solid lines are RCP-provided values.

Atmospheric concentrations of each gas are converted into radiative forcing using the IPCC’s simple radiative forcing functions. When provided with the same atmospheric concentrations as the RCP scenarios, the resulting radiative forcing closely matches RCP scenario forcing, as shown in Figure 3.

Figure 3: Total direct radiative forcing values (CO₂ + CH₄ + N₂O) for the four RCPs and SimMod using the RCP-provided concentrations. Dashed lines are SimMod results; solid lines are RCP-provided values.

Finally, radiative forcing is converted into transient climate response using a continuous diffusion slab ocean model adapted from Caldeira and Myhrvold (2012) and a specified climate sensitivity. The global average temperature is estimated by a weighted average of the ocean model response and the equilibrium temperature response over land. Figure 4 shows the resulting transient temperature response given the RCP scenario forcings compared to the IPCC’s latest climate model runs (CMIP5). The black line is the multi-model mean, while the grey area is the 95% confidence intervals of climate models. The solid red line is the SimMod transient climate response, while the dashed red line represents the equilibrium response (e.g. if there were no oceans to buffer the climate response time).

Figure 4: SimMod transient (solid red) and equilibrium (dashed red) temperature response compared to CMIP5 model results for each RCP.

We hope this model provides a useful tool for researchers looking to move away from simplistic global warming potentials to examine the time-evolution of the temperature response to different emission or mitigation scenarios.

The post SimMod: A simple python based climate model appeared first on Berkeley Earth.

You can read about the latest developments here.

Since 2014 Berkeley Earth has been expanding its real time database of air pollution. Our original focus was on China, but we have continued to add other regions of the world, including Canada

Below see the snapshot from May 7, 2016

A close up view:

Today’s update , available here http://berkeleyearth.org/air-quality-real-time-map/ , shows the change

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For many people, comparing air pollution to cigarette smoking is more vivid and meaningful than is citing the numbers of yearly deaths. When we published our scientific paper on air pollution in China in August 2015¹, we were surprised by the attention we got for a quick comparison we made comparing air pollution on a particularly bad day in Beijing to smoking 1.5 cigarettes every hour. We were also surprised to find that a prominent researcher, Arden Pope, had previously calculated that average pollution in Beijing is similar to smoking 0.3 cigarettes per day – and that this comparison is used to reassure people that the pollution really isn’t that bad.

In this memo, we will derive the rough value of conversion, so people can think of air pollution in terms of cigarettes equivalent. The sole goal of this calculation is to help give people an appreciation for the health effects of air pollution. We will also discuss the apparent discrepancy with Arden Pope (now resolved), which stems from our comparing the health impacts of cigarettes, rather than the amount of PM_2.5 (the most deadly pollutant) delivered.

In summary, we find that air pollution can be approximated as cigarettes equivalent as follows:

Calculation
We start with some numbers estimated by the US Center for Disease Control: 480,000 people die in the US every year due to smoking.² The number of cigarettes sold in the US has been dropping, from 470 billion per year in 1998, to 280 billion per year in 2013. For the purpose of our rough estimate, we will take an average number of 350 billion; it is easy to adjust the numbers using different values.

Now we combine these numbers. The ratio of deaths per year, to cigarettes per year, is 0.00000137, expressed in scientific notation as 1.37 x10^-6. Put another way, there are 1.37 deaths every year for every million cigarettes smoked. We note that this figure agrees with the value of 1.4 published by Bernard Cohen in 1991.³

Now let’s consider air pollution. The most harmful pollution consists of small particulate matter, 2.5 microns in size or less, called PM_2.5. These particles are small enough to work their way deep into the lungs and into the bloodstream, where they trigger heart attack, stroke, lung cancer and asthma. In the Berkeley Earth review of deaths in China we showed that 1.6 million people die every year from an average exposure of 52 μg/m³ of PM_2.5. To kill 1.6 million people would require, assuming 1.37 x10-6 deaths per cigarette, 1.1 trillion cigarettes. Since the population of China is 1.35 billion, that comes to 864 cigarettes every year per person, or about 2.4 cigarettes per day.

Thus the average person in China, who typically breathes 52 μg/m³ of air pollution, is receiving a health impact equivalent to smoking 2.4 cigarettes per day. Put another way, 1 cigarette is equivalent to an air pollution of 22 μg/m³ for one day.

The average PM_2.5 in Beijing over the year is about 85 μg/m³, equivalent to about 4 cigarettes per day. The average value in the industrial city of Handan, about 200 km south of Beijing, is about 120 μg/m³, equivalent to 5.5 cigarettes/day. We were in Beijing when the level rose to 550 μg/m³, equivalent to 25 cigarettes per day. In Harbin, the air pollution has reached the limit of the scale, 999 μg/m³. That would be equivalent to 45 cigarettes per day. As we are writing this, the air pollution in New Delhi, India, is 547 μg/m³, equivalent to about 25 cigarettes each day. A recent peak reported in the Washington Post for the city of Shenyang⁴ set a new record of 1400 μg/m³, equivalent to over three packs of cigarettes per day for every man, woman, and child living there.

Here is the rule of thumb: one cigarette per day is the rough equivalent of a PM_2.5 level of 22 μg/m³. Double that level, and it is equivalent to 2 cigarettes per day. Of course, unlike cigarette smoking, the pollution reaches every age group.

The EPA estimates⁵ that the average air pollution in the United States in 2013 was 9.0 μg/m³. That is equivalent to 0.41 cigarettes per day for every person in the US. From our crude calculation, and taking into account the US population, that average exposure would be expected to lead to 66,000 deaths per year in the US. That is in reasonable agreement with the value of 52,000 per year published by Caiazzo et al. ⁶

Europe’s Environment Commissioner, Janez Potocnik, noted that pollution caused 400,000 premature deaths in 2010 in Europe.⁷ That’s equivalent, for the EU population of 508 million, to everyone smoking 1.6 cigarettes per day.

This sounds bad, but it may be even worse. The US EPA estimates that for every smoking death, there are 30 other people who suffer significant smoking- related health impairment.

Comparison of methods with Arden Pope
Arden Pope had published a number equating the average pollution in Beijing to about 0.3 cigarettes per day, nearly a factor of 10 lower than our value. His value was based on the comparison of the weight of the inhaled component of PM2.5 from smoking cigarettes, not on observed health effects. We discussed his number at some length with him, and he has subsequently gave us permission to quote his current stand as follows: “Although the potential differential toxicity of fine particulate matter air pollution from various sources is not fully understood, fine PM from the burning of coal, diesel, and other fossil fuels as well as high temperature industrial processes may be more toxic than particles from the burning of tobacco.”

For the current memo, rather than just compare the amount of material absorbed in the body, we considered the equivalence of health effects from air pollution and smoking, and that is what our table represents.

Conclusion
Air Pollution kills more people worldwide each year than does AIDS, malaria, diabetes or tuberculosis. For the United States and Europe, air pollution is equivalent in detrimental health effects to smoking 0.4 to 1.6 cigarettes per day. In China the numbers are far worse; on bad days the health effects of air pollution are comparable to the harm done smoking three packs per day (60 cigarettes) by every man, woman, and child. Air pollution is arguably the greatest environmental catastrophe in the world today.

1. Rohde and Muller, 2015, Air Pollution in China: Mapping of Concentrations and Sources, PLOS ONE (available here: http://berkeleyearth.org/air-pollution- overview/)
2. www.cdc.gov/tobacco/data_statistics/fact_sheets/fast_facts/
3. Bernard L. Cohen, 1991, Catalog of Risks Extended and Updated, Health Physics vol. 61, pp. 317-335.
4. http://news.nationalpost.com/news/doomsday-smog-shenyang-records-worst- air-pollution-reading-since-china-started-monitoring-smog
5. http://www.epa.gov/roe/
6. http://dx.doi.org/10.1016/j.atmosenv.2013.05.081
7. http://elpais.com/m/elpais/2013/10/18/inenglish/1382105674_318796.html

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In recent weeks we’ve seen a political controversy over NOAA’s adjustments to temperature records, with accusations from some in congress that records are being changed to eliminate a recent slowdown in warming and to lend support to Obama administration climate policies. This makes it sound like the NOAA record is something of an outlier, while other surface temperature records show more of a slowdown in warming. This is not true; all of the major surface temperature records largely agree on temperatures in recent years. This includes independent groups like Berkeley Earth that receive no government funding. A record warm 2014 and 2015 (to date) has largely eliminated any slowdown in temperatures, whether data is adjusted or not.

Figure 1, below, shows a 12-month running average of temperatures for each of the five series. All are quite similar in trajectory, with only small differences between each series. In all five the last 12 months have been the warmest on record, and 2015 will almost certainly be the warmest year on record in all five.

It’s worth noting that while each group has a different methodology, much of the underlying data is the same. NOAA, the UK’s Hadley Centre, and independent researchers Cowtan and Way (C&W) use effectively the same land data (the Global Historical Climatological Network, or GHCN for short). NASA also uses GHCN data, but adds additional stations in the U.S. and in the Arctic and Antarctic. Berkeley uses a much larger set of stations (about 36,000 stations, compared to around 7,000 for the other records), though NOAA will be switching to a similarly large database of stations soon. Berkeley, Hadley, and C&W all use a sea surface temperature series called HadSST3 produced by the Hadley Centre. NOAA and NASA use a sea surface temperature series called ERSST (version 4) produced by NOAA.

Automated adjustments to land data to remove detected problems like station moves or instrument changes are used by NOAA, NASA, and Berkeley Earth; Hadley and C&W do relatively little adjustments to land data outside of quality control. NOAA and Hadley only calculate temperatures in areas with nearby stations, while NASA, Berkeley, and C&W fill in areas without stations based on statistical techniques using the nearest available stations.

No matter which groups’ record you use, you end up with a pretty similar global temperature record. Figure 2, below, shows the trend in temperatures for three periods: 1950-present, 1970-present, and the nominal “slowdown” period of 1998-present. It shows that while the 1998-present period is warming a tad slower than the 1970-present period on average, the uncertainties are large and the warming rate over the post-1998 period is pretty much the same as the longer 1950-present period.

The relatively lower 1998-present trends are also a result of cherry-picking the 1998 El Nino as a start date (since temperature were anomalously high at that point, the trend thereafter will be lower than starting before or after the El Nino event). For example, calculated trends from 1996 or 2000-present are more similar to the 1970-present trends.

The actual adjustments that NOAA does to the record have a relatively small impact on temperatures in recent years, though small changes can have outsized impacts when calculating short-term trends. The larger impacts of NOAA adjustments by far are in the early part of the record, where they raise temperatures compared to the unadjusted series. Contrary to what most folks assume, the net effect of adjustments is to reduce, not increase, the amount of warming that we’ve experienced over the past century.

The fact that independent groups like Berkeley Earth find results nearly identical to NOAA should help put to rest and lingering concerns that some nefarious scheme has been hatched among scientists to cook the proverbial book. Rather, temperature data is complex and inhomogeneous, coming from multiple different sources and instruments over the past 250 years. Some adjustments are needed when switching from buckets to ship engine intake valves to buoys, as each will read temperatures a bit differently. The overall effect of these adjustments is small on a global level, however, and they have relatively little bearing on our understanding of modern warming.

The post Nature Not NOAA Ended the Slowdown
in Temperatures appeared first on Berkeley Earth.

In our recent study in PLOS on air pollution in China we estimated that the Chinese residents are typically exposed to roughly 50 micrograms per cubic meter and that this exposure results in roughly 1.6 million deaths per year, or 17% of all deaths. The US EPA recommends that long-term exposure to this particulate matter be limited to no more than 12 micrograms per cubic meter.

Concentrations of 1000 to 1400, roughly 100 times want the EPA advises, are hard to fathom. A picture is worth 1400 words

In terms of health effects we could put it this way: breathing 1400 micrograms is the equivalent of every person smoking roughly 3 packs of cigarettes a day, or 60 cigarettes.

As Live Science reports:

That much pollution is “a big deal,” said Dr. Norman Edelman, a senior consultant for scientific affairs with the American Lung Association.

Fine particulate matter is dangerous for human health because the particles are so tiny that they can bypass the body’s normal defense systems, such as the mucus membranes that line the mouth and nose. The particles can penetrate deep into the lungs, and sometimes can even pass through the tissue of the lungs and enter the bloodstream, Edelman said.

Particulate pollution is hard to escape because its sources are so prevalent in modern cities and towns. But breathing in these superfine particles damages the respiratory tract, experts say, and it can worsen people’s pre-existing conditions and increase the risk of new  infections.

If you look at an area that is subjected to spikes in pollution, you’ll see an increase in hospital admissions for lung and heart disease.

The peak values recorded in Shenyang don’t tell the entire story. Levels throughout the prefecture hit hazardous levels. Below see an map of PM_2.5 in China recorded at the time of the peak pollution.

As the chart indicates the values exceeded the highest EPA classification of “Hazardous” and were not confined to the city.  In fact, these are the highest levels Berkeley Earth has observed anywhere in China during the 19 months that we have been archiving real-time observations.

In Northeastern China, it is not uncommon for air pollution to spike October / November. At this time of year, many municipal heating systems reactivate their coal-burning boilers for the winter. That activation process is accompanied by a large surge in fine particulate emissions, much higher than normal operating conditions. That activation, coupled with weather conditions that trapped particulates at low altitudes, is likely to be the immediate cause of this year’s historic haze in Shenyang. A similar, but less severe peak was seen at a similar time last year:

Currently Berkeley Earth is continuing its research into air pollution collecting real time data from China and other parts of the far east, while expanding our scope to include european states. Current PM_2.5 conditions for China and Japan can be observed on our real-time map. As regions are added they will be included in the real time maps and added to the data archives located here.

The post 3 Packs a day: Killer Air in Shenyang appeared first on Berkeley Earth.

“Uncertainty in data set production can result from the choice of parameters within a particular analytical framework, parametric uncertainty, or from the choice of overall analytical framework, structural  uncertainty. Structural uncertainty is best estimated by having multiple independent groups assess the same  data using distinct approaches. More analyses assessed now than at the time of AR4 include a published  estimate of parametric or structural uncertainty. It is important to note that the literature includes a very broad range of approaches. Great care has been taken in comparing the published uncertainty ranges as they  almost always do not constitute a like-for-like comparison. In general, studies that account for multiple  potential error sources in a rigorous manner yield larger uncertainty ranges.”   ( Draft)

We will return to that issue, but first our results to date along with a estimate of how the year will end. We start with the land component.

While our record goes back to 1750, here we only show from 1850 to present. While we calculate an absolute temperature field, in order to compare with other dataset producers we present anomalies based on 1961-1990 period. The estimate for the year end ( the green bars ) is calculated by looking at the difference between  Jan-Sept  and Jan-Dec  for every year in the record. This difference and its uncertainty is combined with the Jan2015-Sept2015  estimate to produce an prediction for the entire year. As the green bar indicates there is a modest probability that 2015 land temperatures will set a record.

In addition to a land product, we also create an ocean temperature product. The source data here is HADSST, however we apply our own interpolation.

The estimate for ocean temperature indicates a large probability of a record breaking year.

When we combine the Surface Air Temperature (SAT) above the land with an ocean temperature product and form a global temperature index we see the following:

Our approach projects an 85% likelihood that 2015 will be a record year.  When we speak of records we can only speak of a record given our approach and given our data.  We also need to be aware that focusing on record years can sometimes obscure the bigger point of the data. It is clear that the earth has warmed since the beginning of record keeping. That is clear in the land record. It is clear in the ocean temperature record, and when we combine those records into the global index we see the same story. Record year or not, the entirety of the data shows a planet warming at or near the surface.

Structural Uncertainty

As the quote from the draft version of AR5  indicated there is also an issue of structural uncertainty.  Wikipedia gives a serviceable definition:

 “Structural uncertainty, aka model inadequacy, model bias, or model discrepancy, which comes from the lack of knowledge of the underlying true physics. It depends on how accurately a mathematical model describes the true system for a real-life situation, considering the fact that models are almost always only approximations to reality. One example is when modeling the process of a falling object using the free-fall model; the model itself is inaccurate since there always exists air friction. In this case, even if there is no unknown parameter in the model, a discrepancy is still expected between the model and true physics.”

It may seem odd to talk about the global temperature index as a “model”, but at it’s core every global average is a model, a data model. What is being “modelling” in all the approaches is this:  the temperature where we don’t have measurements. Another word for this is interpolation. We have measurements at a finite number of locations and we use that information to predict the temperature at locations where we have no thermometers. For example, CRU uses a gridded approach where stations within a grid cell are averaged. Physically this is saying that latitude and longitude determine the temperature.  Grid cells with no stations are simply left empty.  Berkeley uses a different approach that looks at the station locations and interpolates amongst them using the expected correlations between stations.  This allows us to populate more of the map, and avoids biases due to the arbitrary placement of grid cell boundaries.

If we like, we can assess the structural uncertainty by comparing methods using the same input data: We did that here, and compared the Berkeley method with the GISS method and CRU method. That test shows the clear benefits of the BE approach. Given the same data the BE predictions of temperatures at unmeasured locations has a lower error than the GISS approach or the CRU approach.

However, comparisons between various methods are usually not so clean. In most cases, not only is the method different, but the data is different as well.  Below see a comparison with the other indices, focused in particular on the last 15 years.

The differences we see between the various approaches comes down to two factors: Differences in datasets and differences in methods. While all four records fall within the uncertainty bands, it appears as if NCDC does have an excursion outside this region; and if we look towards years end, it appears that their record shows more warmth than others.

In order to understand this we took a closer look at NCDC. I will start with some material we created while doing our original studies. Over the course of the climate debate some skeptics and journalists have irresponsibly insinuated that the GHCN records  have been “manipulated”. This is important because CRU and GISS and NCDC all use GHCN records. We can test that hypothesis of “manipulation” by comparing what our method shows using  GHCN data and then comparing that with non-GHCN data.  If the hypothesis of “manipulation” were true, we would expect to find differences  or evidence that the record was being “manipulated”. We reject that hypothesis.

This implies is that the reason for the difference between NCDC and BE probably doesn’t lie entirely in the choice of data since we get the same answer whether we use GHCN data or not. It suggests that the NCDC method or some interaction of data and method is the reason for the difference between BE and NCDC.

Since NCDC use a gridded approach  there is the possibility that the selection of grid size is driving the difference. We see a similar effect with CRU which uses a 5 degree grid. That choice results in grid cells with no estimate. In short, they don’t estimate the global temperature. To examine this we start by looking at the Berkeley global field:

There are two things to note here. First the large positive anomaly in the Arctic and second the cool anomalies at the South Pole. In some years we will see that CRU has a lower global anomaly because they do not estimate where the world is warming the fastest: the Arctic. This year, however, we have the opposite effect with NCDC. They are warmer because of missing grid cells at the South Pole. The “choice” of grid cell size influences the answer: As we can see some years the choice of grid cell size results in a warmer record, and other years a cooler record.

Since NCDC uses GHCN data and since they use a gridded approach with grid cells that are too small, they end up with no estimate for the cooling South Pole. The end result is a temperature index that runs hotter than other approaches. For example, both GISS and Berkeley Earth use data from SCAR for Antarctica. When you combine SCAR with GHCN as both BE and GISS do, there are 86 stations with at least one month of data in 2015.

GISS can be viewed here. With 1200km gridding the global average anomaly for September is  .68C. If we switch to 250km gridding, the anomaly increases to .73C. This year, less interpolation results in a warmer estimate. Depending on the year and depending on the warming or cooling at either pole, the very selection of a grid size can change the estimated anomaly. Critics of interpolation may have to rethink their objections.

2015 looks like it is shaping up to be an interesting year both from perspective of “records” and from the perspective of understanding how different data and different methods can result in slightly different answers. And it’s most interesting because it may lead people to understand that interpolation or infilling can lead to both warmer records and cooler records depending on the structure of warming across the globe.

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As Richard Muller writes:

“The pieces are all fitting together; the picture is emerging from the jigsaw puzzle.  The case is getting stronger that air pollution around the world is a severe, perhaps the most severe environmental disaster in the world today.  Someday it might be overtaken by global warming, but air pollution is today’s killer. Worldwide, PM2.5 kills more people per year than AIDS, malaria, diabetes or tuberculosis, and its effects are most damaging in the developing world. But even in the US, air pollution is responsible every year for more deaths than those caused by automobile accidents.”

The Lelieveld work provides an important complement to our study. Both studies used the same WHO approach to estimating mortality from PM2.5 concentrations; however, the two papers took different approaches to estimating   concentrations. BerkeleyEarth worked from ground station data: air quality measurements taken at ground level at over 900 locations in China. We recorded the actual concentration levels in situ. The Lelieveld study took a different approach.

Working from the Emissions Database for Global Atmospheric Research (EDGAR) which estimates source emissions and combining that with a weather and chemical transport model, Lelieveld et al, were able to estimate concentrations globally. This approach has uncertainties since the data relies on estimates of emissions and their sources, not to mention the uncertainties involved in estimating transport. Nevertheless, the estimates they obtained for mortality match with our estimates for mortality. Since the results match in areas where we have actual observations of PM2.5 concentrations we have some measure of confidence in their global numbers

On benefit of the modelling approach  is that it allows you to make estimates in areas where you have no air quality measurement systems. As BerkeleyEarth continues to expand it collection of obsveration from Japan,India, Europe and other parts of the world, we will be in a position to report on the accuracy of the modelling in those parts of the world

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