Ars Technica: Thorough, not thoroughly fabricated: The truth about global temperature data

How thermometer and satellite data is adjusted and why it must be done.
published on Ars Technica by Scott K. Johnson - Jan 21, 2016 4:30pm CET

“In June, NOAA employees altered temperature data to get politically correct results.”

At least, that's what Congressman Lamar Smith (R-Tex.) alleged in a Washington Post letter to the editor last November. The op-ed was part of Smith's months-long campaign against NOAA climate scientists. Specifically, Smith was unhappy after an update to NOAA’s global surface temperature dataset slightly increased the short-term warming trend since 1998. And being a man of action, Smith proceeded to give an anti-climate change stump speech at the Heartland Institute conference, request access to NOAA's data (which was already publicly available), and subpoena NOAA scientists for their e-mails.

Smith isn't the only politician who questions NOAA's results and integrity. During a recent hearing of the Senate Subcommittee on Space, Science, and Competitiveness, Senator Ted Cruz (R-Tex.) leveled similar accusations against the entire scientific endeavor of tracking Earth’s temperature.

“I would note if you systematically add, adjust the numbers upwards for more recent temperatures, wouldn’t that, by definition, produce a dataset that proves your global warming theory is correct? And the more you add, the more warming you can find, and you don’t have to actually bother looking at what the thermometer says, you just add whatever number you want.”

There are entire blogs dedicated to uncovering the conspiracy to alter the globe's temperature. The premise is as follows—through supposed “adjustments,” nefarious scientists manipulate raw temperature measurements to create (or at least inflate) the warming trend. People who subscribe to such theories argue that the raw data is the true measurement; they treat the term “adjusted” like a synonym for “fudged.”

Peter Thorne, a scientist at Maynooth University in Ireland who has worked with all sorts of global temperature datasets over his career, disagrees. “Find me a scientist who’s involved in making measurements who says the original measurements are perfect, as are. It doesn’t exist,” he told Ars. “It’s beyond a doubt that we have to—have to—do some analysis. We can’t just take the data as a given.”

Speaking of data, the latest datasets are in and 2015 is (as expected) officially the hottest year on record. It's the first year to hit 1°C above levels of the late 1800s. And to upend the inevitable backlash that news will receive (*spoiler alert*), using all the raw data without performing any analysis would actually produce the appearance of more warming since the start of records in the late 1800s.

We're just taking the temperature—how hard can it be?

So how do scientists build datasets that track the temperature of the entire globe? That story is defined by problems. On land, our data comes from weather stations, and there’s a reason they are called weather stations rather than climate stations. They were built, operated, and maintained only to monitor daily weather, not to track gradual trends over decades. Lots of changes that can muck up the long-term record, like moving the weather station or swapping out its instruments, were made without hesitation in the past. Such actions simply didn’t matter for weather measurements.

The impacts of those changes are mixed in with the climate signal you’re after. And knowing that, it’s hard to argue that you shouldn’t work to remove the non-climatic factors. In fact, removing these sorts of background influences is a common task in science. As an incredibly simple example, chemists subtract the mass of the dish when measuring out material. For a more complicated one, we can look at water levels in groundwater wells. Automatic measurements are frequently collected using a pressure sensor suspended below the water level. Because the sensor feels changes in atmospheric pressure as well as water level, a second device near the top of the well just measures atmospheric pressure so daily weather changes can be subtracted out.

If you don't make these sorts of adjustments, you’d simply be stuck using a record you know is wrong.

You can continue reading at Ars Technica. The article still explains several reasons for inhomogeneities in the temperature observations, how they are removed with statistical homogenization methods, how these methods have been validated, the uncertainties in the sea surface temperature and satellite estimates of the tropospheric temperature and why it is so hard to get the right. It finishes with the harassment campaign against NOAA of Lamar Smith because of a minimal update.

Enjoy and bookmark, it is a very thorough and accessible overview.

The transition to automatic weather stations. We’d better study it now.

This is a POST post.

The Parallel Observations Science Team (POST) is looking across the world for climate records which simultaneously measure temperature, precipitation and other climate variables with a conventional sensor (for example, a thermometer) and modern automatic equipment. You may wonder why we take the painstaking effort of locating and studying these records. The answer is easy: the transition from manual to automated records has an effect on climate series and the analysis we do over them.

In the last decades we have seen a major transition of the climate monitoring networks from conventional manual observations to automatic weather stations. It is recommended to compare these instruments before the substitution is effective with side by side measurements, which we call parallel measurements. Climatologists have also set up many longer experimental parallel measurements. They tell us that in most cases both sensors do not measure the same temperature or collect the same amount of precipitation. A different temperature is not only due to the change of the sensor itself, but automatic weather stations also often use a different, much smaller, screen to protect the sensor from the sun and the weather. Often the introduction of automatic weather stations is accompanied by a change in location and siting quality.

From studies of single temperature networks that made such a transition we know that it can cause large jumps; the observed temperatures at a station can go up or down by as much as 1°C. Thus potentially this transition can bias temperature trends considerably. We are now trying to build a global dataset with parallel measurements to be able to quantify how much the transition to automatic weather stations influences the global mean temperature estimates used to study global warming.

Temperature

This study is led by Enric Aguilar and the preliminary results below were presented at the Data Management Workshop in Saint Gallen, Switzerland last November. We are still in the process of building up our dataset. Up to now we have data from 10 countries: Argentina (9 pairs), Australia (13), Brazil (4), Israel (5), Kyrgyzstan (1), Peru (31), Slovenia (3), Spain (46), Sweden (8), USA (6); see map below.


Global map in which we only display the 10 countries for which we have data. The left map is for the maximum temperature (TX) and the right for the minimum temperature (TN). Blue dots mean that the automatic weather station (AWS) measures cooler temperatures than the conventional observation, red dots mean the AWS is warmer. The size indicates how large the difference is, open circles are for statistically not significant differences.

The impact of the automation can be better assessed in the box plots below.


The bias of the individual pairs are shown as dots and summarized per country with box plots. For countries with only a few pairs the boxplots should be taken with a grain of salt. Negative values mean that the automatic weather stations are cooler. We have data for Argentina (AR), Australia (AU), Brazil (BR), Spain (ES), Israel (IL), Kyrgyzstan (KG), Peru (PE), Sweden (SE), Slovenia (SI) and the USA (US). Panels show the maximum temperature (TX), minimum temperature (TN), mean temperature (TM) and Diurnal temperature range (DTR, TX-TN).

On average there are no real biases in this dataset. However, if you remove Peru (PE) the differences in the mean temperature are either small or negative. That one country is so important shows that our dataset is currently too small.

To interpret the results we need to look at the main causes for the differences. Important reasons are that Stevenson screens can heat up in the sun on calm days, while automatic sensors are sometimes ventilated. The automatic sensors are, furthermore, typically smaller and thus less affected by direct radiation hitting them than thermometers. On the other hand, in case of conventional observation, the maintenance of the Stevenson screens—cleaning and painting—and detection of other problems may be easier because they have to be visited daily. There are concerns that plastic screens get more grey and heat more in the sun. Stevenson screens have more thermal inertia, they smooth fast temperature fluctuations, and will thus show lower highs and higher lows.

Also the location often changes with the installation of automatic weather stations. America was one of the early adopters. The US National Weather Service installed analogue semi-automatic equipment (MMTS) that did not allow for long cables between the sensor and the display inside a building. Furthermore, the technicians only had one day per station and as a consequence many of the MMTS systems were badly sited. Nowadays technology has advanced a lot and made it easier to find good sites for weather stations. This is maybe even easier now than it used to be for manual observations because modern communication is digital and if necessary uses radio making distance much less a concern. The instruments can be powered by batteries, solar or wind, which frees them from the electricity grid. Some instruments store years of data and need just batteries.

In the analysis we thus need to consider whether the automatic sensors are placed in Stevenson screens and whether the automatic weather station is at the same location. Where the screen and the location did not change (Israel and Slovenia), the temperature jumps are small. Whether the automatic weather station reduces radiation errors by mechanical ventilation is likely also important. Because of these different categories, the number of datasets needed to get a good global estimate becomes larger. Up to now, these factors seem to be more important than the climate.

Precipitation

For most of these countries we also have parallel measurements for precipitation. The figure below was made by Petr Stepanek, who leads this part of the study.


Boxplots for the differences in monthly precipitation sums due to automation. Positive values mean that the manual observations record more precipitation. Countries are: Argentina (AG), Brazil (BR), The Check Republic (CZ), Israel (IS), Kyrgyzstan (KG), Peru (PE), Sweden (SN), Spain (SP) and the USA (US). The width of the boxplots corresponds to the size of the given dataset.

For most countries the automatic weather stations record less precipitation. This is mainly due to smaller amounts of snow during the winter. Observers often put a snow cross in the gauge in winter to make it harder for snow to blow out of it again. Observers simply melt the snow gathered in a pot to measure precipitation, while early automatic weather stations did not work well with snow and sticky snow piling up in the gauge may not be noticed. These problems can be solved by heating the gauge, but unfortunately the heater can also increase the amount of precipitation that evaporates before it could be registered. Such problems are known and more modern rain gauges use different designs and likely have a smaller bias again.

Database with parallel data

The above results are very preliminary, but we wanted to show the promise of a global dataset with parallel data to study biases in the climate record due to changes in the observing practises. To proceed we need more datasets and better information on how the measurements were performed to make this study more solid.

In future we also want to look more at how the variability around the mean is changing. We expect that changes in monitoring practices have a strong influence on the tails of the distribution and thus on estimates of changes in extreme weather. Parallel data offer a unique opportunity to study this otherwise hard problem.

Most of the current data comes from Europe and South America. If you know of any parallel datasets especially from Africa or Asia, please let us know. Up to now, the main difficulty for this study is to find the persons who know where the data is. Fortunately, data policies do not seem to be a problem. Parallel data is mostly seen as experimental data. In some cases we “only” got a few years of data from a longer dataset, which would otherwise be seen as operational data.

We would like to publish the dataset after publishing our papers about it. Again this does not seem to lead to larger problems; sometimes people prefer to first publish an article themselves, which causes some delays, and sometimes we cannot publish the daily data itself, but “only” monthly averages and extreme value indices, this makes the results less transparent, but these summary values contain most of the information.

Knowledge of the observing practices is very important in the analysis. Thus everyone who contributes data is invited to help in the analysis of the data and co-author our first paper(s). Our studies are focused on global results, but we will also provide everyone with results for their own dataset to gain a better insight into their data.

Most climate scientists would agree that it is important to understand the impact of automation on our records. So does the World Meteorological Organization. In case it helps you to convince your boss: the Parallel Observations Science Team is part of the International Surface Temperature Initiative (ISTI). It is endorsed by the Task Team on Homogenization (TT-HOM) of the World Meteorological Organization (WMO).

We expect that this endorsement and our efforts to raise awareness about our goals and their importance will help us to locate and study parallel observations from other parts of the world, especially Africa and Asia. We also expect to be able to get more data from Europe; the regional association for Europe of the WMO has designated the transition to automatic weather stations as one of its priorities and is helping us to get access to more data. We want to have datasets for all over the world to be able to assess whether the station settings (sensors, screens, data quality, etc.) have an impact, but also to understand if different climates produce different biases.

If you would like to collaborate or have information, please contact me.

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Related reading

The ISTI has made a series of brochures on POST in English, Spanish, French and German. If anyone is able to make further translations, that would be highly appreciated.

Parallel Observations Science Team of the International Surface Temperature Initiative.

Irrigation and paint as reasons for a cooling bias

Temperature trend biases due to urbanization and siting quality changes

Changes in screen design leading to temperature trend biases

Temperature bias from the village heat island

Interesting EGU sessions and conferences in 2016

Just a quick post to advertise some interesting (new) EGU sessions and conferences this year.

At EGU there will be four interesting sessions that fit to the topic of this blog. The abstract deadline is already next Wednesday, the 13th of January at 13 CET. The conference is half April in Vienna, Austria.

Climate Data Homogenization and Climate Trend and Variability Assessment
The main session for all things homogenization.

Taking the temperature of Earth: Variability, trends and applications of observed surface temperature data across all domains of Earth's surface
On measuring temperatures: surface itself (skin temperature), surface air over land, see surface temperature, marine air temperature. With a large range of observational methods, including satellites.

Transition into the Anthropocene-causes of climate change in the 19th and 20th century
A session on climate change in the very challenging early instrumental period, where the variability of station observations has large uncertainties. This session is new, as far as I can see. But EGU is big, I hope I did not miss it last year.

Historical Climatology
Even further back in time is the session on historical climatology where people mainly look at non-instrumental evidence of climatic changes and their importance for human society.

Also this year there will be an EMS conference. Like every second year, in 2016 it will be combined with the European Conference on Applied Climatology (ECAC) and thus has more climate goodies than average. This year it will be half September in Trieste, Italy.

The main session for fans of homogenization is: Climate monitoring; data rescue, management, quality and homogenization.

Fans of variability may like the session on Spatial Climatology.

A conference I really enjoyed the last two times I was there is the International Meeting on Statistical Climatology. Its audience is half statisticians and half climatologists. Everyone loves beautiful statistical and methodological questions. Great!! This year is will be in June in Canmore, Alberta, Canada.

It also has a session on homogenization: Climate data homogenization and climate trends/variability assessment.

If I missed any interesting sessions or conferences do let us know in the comments (also if it is your own).

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Descriptions

Climate Data Homogenization and Climate Trend and Variability Assessment
Convener: Xiaolan L. Wang
Co-Conveners: Enric Aguilar, Rob Roebeling, and Petr Stepanek

The accuracy and homogeneity of climate data are indispensable for many aspects of climate research. In particular, a realistic and reliable assessment of historical climate trends and variability is hardly possible without a long-term, homogeneous time series of climate data. Accurate and homogeneous climate data are also indispensable for the calculation of related statistics that are needed and used to define the state of climate and climate extremes. Unfortunately, many kinds of changes (such as instrument and/or observer changes, and changes in station location and environment, observing practices and procedure, etc.) that took place in the period of data record could cause non-climatic changes (artificial shifts) in the data time series. Such artificial shifts could have huge impacts on the results of climate analysis, especially those of climate trend analysis. Therefore, artificial changes shall be eliminated, to the extent possible, from the time series prior to its application, especially its application in climate trends assessment.

This session calls for contributions that are related to bias correction and homogenization of climate data, including bias correction and validation of various climate data from satellite observations and from GCM and RCM simulations, as well as quality control/assurance of observations of various variables in the Earth system. It also calls for contributions that use high quality, homogeneous climate data to assess climate trends and variability and to analyze climate extremes, including the use of bias-corrected GCM or RCM simulations in statistical downscaling. This session will include studies that inter-compare different techniques and/or propose new techniques/algorithms for bias-correction and homogenization of climate data, for assessing climate trends and variability and analysis of climate extremes (including all aspects of time series analysis), as well as studies that explore the applicability of techniques/algorithms to data of different temporal resolutions (annual, monthly, daily) and of different climate elements (temperature, precipitation, pressure, wind, etc) from different observing network characteristics/densities, including various satellite observing systems.

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Transition into the Anthropocene-causes of climate change in the 19th and 20th century
Convener: Gabriele Hegerl
Co-Convener: Stefan Brönnimann

This session focuses on the long view of climate variability and change as available from long records, reconstructions, reanalysis efforts and modelling, and we welcome analysis of temperature, precipitation, extreme events, sea ice, and ocean. Contributions are welcome that evaluate changes from historical data on the scale of large regions to the globe, analyse particular unusual climatic events, estimate interdecadal climate variability and climate system properties from long records, attribute causes to early observed changes and model or data assimilate this period. We anticipate that bringing observational, modelling and analysis results together will improve understanding and prediction of the interplay of climate variability and change. "

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Taking the temperature of Earth: Variability, trends and applications of observed surface temperature data across all domains of Earth's surface
See also their homepage.
Convener: Darren Ghent
Co-Conveners: Nick Rayner, Stephan Matthiesen, Simon Hook, G.C. Hulley, Janette Bessembinder

Surface temperature (ST) is a critical variable for studying the energy and water balances of the Earth surface, and underpinning many aspects of climate research and services. The overarching motivation for this session is the need for better understanding of in-situ measurements and satellite observations to quantify ST. The term "surface temperature" encompasses several distinct temperatures that differently characterize even a single place and time on Earth’s surface, as well as encompassing different domains of Earth’s surface (surface air, sea, land, lakes and ice). Different surface temperatures play inter-connected yet distinct roles in the Earth’s surface system, and are observed with different complementary techniques.

The EarthTemp network was established in 2012 to stimulate new international collaboration in measuring and better understanding ST across all domains of the Earth’s surface including air, land, sea, lakes, ice. New and existing international projects and products have evolved from network collaboration (e.g. ESA Climate Change Initiative SST project, EUSTACE, FIDUCEO, International Surface Temperature Initiative, ESA GlobTemperature, HadISST, CRUTEM and HadCRUT). Knowledge gained during this EarthTemp session will be documented and published as part of the user requirements exercises for such projects and will thus benefit the wider community. A focus of this session is the use of ST's for assessing variability and long-term trends in the Earth system. In addition there will be opportunity for users of surface temperature over any surface of Earth on all space and timescales to showcase their use of the data and their results, to learn from each others' practice and to communicate their needs for improvements to developers of surface temperature products. Suggested contributions can include, but are not limited to, topics like:

* The application of ST in climate science
* How to improve remote sensing of ST in different environments
* Challenges from changes of in-situ observing networks over time
* Current understanding of how different types of ST inter-¬relate
* Nature of errors and uncertainties in ST observations
* Mutual/integrated quality control between satellite and in-situ observing systems.
* What do users of surface temperature data require in practical applications?

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Historical Climatology
Convener: Stefan Grab
Co-Conveners: Rudolf Brazdil, David Nash, Georgina Endfield

Historical Climatology has gained momentum and worldwide recognition over the last couple of decades, particularly in the light of rapid global climate and environmental change. It is now well recognized that in order to better project future changes and be prepared for those changes, one should look to, and learn from, the past. To this end, historical documentary sources, in many cases spanning back several hundred years and far beyond instrumental weather records, offer detailed descriptive (qualitative) accounts on past weather and climate. Such documentary sources typically include, amongst others: weather diaries, ship log books, missionary reports and letters, historical newspapers, chronicles, accounting and government documents etc. Such proxies have particular advantages in that they in most cases offer details on the specific timing and placement of an event. In addition, valuable insights may be gained on environmental and anthropogenic consequences and responses to specific weather events and climate anomaly. Similarly, oral history records, based on people’s personal accounts and experiences of past weather offer important insights on perceptions of climate change, and details on past and sometimes ‘forgotten’ weather events and their consequences.

This session welcomes all studies using documentary, historical instrumental and oral history based approaches to: produce historical climate chronologies (multi-decadal to centennial scale), gain insights into past climatic periods or specific weather events, detail environmental and human consequences to past climate and weather, share people’s experiences and perceptions of past climate, weather events and climate change, and reflect on lessons learnt (coping and adaptation) from past climate and weather events. Whilst welcoming contributions from all global regions, we particularly appeal for contributions from Asia and the Middle East.

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Climate monitoring; data rescue, management, quality and homogenization
Convener: Manola Brunet-India
Co-Conveners: Hermann Mächel, Victor Venema, Ingeborg Auer, Dan Hollis

Robust and reliable climatic studies, particularly those assessments dealing with climate variability and change, greatly depend on availability and accessibility to high-quality/high-resolution and long-term instrumental climate data. At present, a restricted availability and accessibility to long-term and high-quality climate records and datasets is still limiting our ability to better understand, detect, predict and respond to climate variability and change at lower spatial scales than global. In addition, the need for providing reliable, opportune and timely climate services deeply relies on the availability and accessibility to high-quality and high-resolution climate data, which also requires further research and innovative applications in the areas of data rescue techniques and procedures, data management systems, climate monitoring, climate time-series quality control and homogenisation.

In this session, we welcome contributions (oral and poster) in the following major topics:

• Climate monitoring , including early warning systems and improvements in the quality of the observational meteorological networks

• More efficient transfer of the data rescued into the digital format by means of improving the current state-of-the-art on image enhancement, image segmentation and post-correction techniques, innovating on adaptive Optical Character Recognition and Speech Recognition technologies and their application to transfer data, defining best practices about the operational context for digitisation, improving techniques for inventorying, organising, identifying and validating the data rescued, exploring crowd-sourcing approaches or engaging citizen scientist volunteers, conserving, imaging, inventorying and archiving historical documents containing weather records

• Climate data and metadata processing, including climate data flow management systems, from improved database models to better data extraction, development of relational metadata databases and data exchange platforms and networks interoperability

• Innovative, improved and extended climate data quality controls (QC), including both near real-time and time-series QCs: from gross-errors and tolerance checks to temporal and spatial coherence tests, statistical derivation and machine learning of QC rules, and extending tailored QC application to monthly, daily and sub-daily data and to all essential climate variables

• Improvements to the current state-of-the-art of climate data homogeneity and homogenisation methods, including methods intercomparison and evaluation, along with other topics such as climate time-series inhomogeneities detection and correction techniques/algorithms (either absolute or relative approaches), using parallel measurements to study inhomogeneities and extending approaches to detect/adjust monthly and, especially, daily and sub-daily time-series and to homogenise all essential climate variables

• Fostering evaluation of the uncertainty budget in reconstructed time-series, including the influence of the various data processes steps, and analytical work and numerical estimates using realistic benchmarking datasets

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Spatial Climatology
Convener: Ole Einar Tveito
Co-Conveners: Mojca Dolinar, Christoph Frei

Gridded representation of past and future weather and climate with high spatial and temporal resolution is getting more and more important for assessing the variability of and impact of weather and climate on various environmental and social phenomena. They are also indispensable as validation and calibration input for climate models. This increased demand requires new efficient methods and approaches for estimating spatially distributed climate data as well as new efficient applications for managing and analyzing climatological and meteorological information at different temporal and spatial scales. This session addresses topics related to generation and application of gridded climate data with an emphasis on statistical methods for spatial analysis and spatial interpolation applied on observational data.

An important aspect in this respect is the creation and further use of reference climatologies. The new figures calculated for the latest normal period 1981-2010 are now recommended as reference period for assessments of regional and local climatologies. For this period new observation types (e.g. satellite and radar data) are available, and contributions taking advantage of multiple data sources are encouraged.

Spatial analysis using e.g. GIS is a very strong tool for visualizing and disseminating climate information. Examples showing developments, application and products of such analysis for climate services are particularly welcome.

The session intends to bring together experts, scientists and other interested people analyzing spatio-temporal characteristics of climatological elements, including spatial interpolation and GIS modeling within meteorology, climatology and other related environmental sciences.

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Climate data homogenization and climate trends/variability assessment
Convener: Xiaolan Wang, Lucie Vincent, Markus Donat and Lisa Alexander

The accuracy and homogeneity of climate data are indispensable for many aspects of climate research. In particular, a realistic and reliable assessment of historical climate trends and variability is hardly possible without a long-term, homogeneous time series of climate data. Accurate and homogeneous climate data are also indispensable for the calculation of related statistics that are needed and used to define the state of climate and climate extremes. Unfortunately, many kinds of changes (such as instrument and/or observer changes, and changes in station location and exposure, observing practices and procedure, etc.) that took place in the period of data record could cause non-climatic sudden changes (artificial shifts) in the data time series. Such artificial shifts could have huge impacts on the results of climate analysis, especially those of climate trend analysis. Therefore, artificial changes shall be eliminated, to the extent possible, from the time series prior to its application, especially its application in climate trends assessment.

This session calls for contributions that are related to bias correction and homogenization of climate data, including bias correction and validation of various climate data from satellite observations and from GCM and RCM simulations, as well as quality control/assurance of observations of various variables in the Earth system. It also calls for contributions that use high quality, homogeneous climate data to assess climate trends and variability and to analyze climate extremes, including the use of bias-corrected GCM or RCM simulations in statistical downscaling.

Harassment and that powerful male PI

After decades of sexual harassment astronomer Geoffrey Marcy has been fired. What surprised me reading many comments was how invincible he was thought to be. I am not nearly as powerful, but can maybe still offer a view from the other side. My impression is naturally subjective and I work in a different field and in another country, but I wonder if Geoffrey Marcy was really that invincible. Unfortunately, people having that impression is partially a self-fulfilling prophesy. Making this picture more realistic may thus help reduce harassment, which is why I wanted to write about this.

Your scientific network is crucial

This impression of invincibility may partially stem from the lone genius syndrome in the media, which is not even right for Einstein. To make a story more pleasant to read journalists like to personalize everything and simplify their story enormously, which leaves little space for history and multiple contributors.

More realistic is that it is very hard to do science in isolation. Feedback is very important. If you only get feedback after publication, you will progress very slowly. Collaboration is also important to get new skills into a study. Understanding new methods and datasets takes a long time. Without collaboration you would have to invest a lot of time and still have a high risk of making rookie mistakes. Without collaboration your productivity and quality will be much lower.

Also the peer review of research proposal and scientific articles makes it important to keep friendly relationships with most colleagues. A research proposal always has weaknesses; if everything was clear already it would not be a science project. Even if not, a reviewer can always claim a proposal is too ambitious or not ambitious enough. Judging a proposal to be “very good” rather than “excellent” is always possible and reduces the funding probability a lot given the low percentage that is funded.

Negotiating conflicts

Due to the importance of collaboration and good relationships open conflicts are rare. There are open debates about specific scientific questions; questions that have an answer and enhance your scientific reputation if you are right. Vaguer questions that do not have an answer are not worth an open conflict that may damage relationships.

That normally most scientists are on talking terms with each other may give outsiders and young scientists the feeling that the seniors are one solid block. This is certainly not the case. Inside you can see the cracks. Most conflicts between groups are for long-forgotten historical reasons. Most conflicts between people because their personalities do not match.

(Hardly ever do you see conflicts for political reasons. I just send an email to Iran and got a friendly answer. My colleague from Serbia still talks to me after we bombed his country based on flimsy evidence. As far as I can judge the climate “debate” is nearly never a source of conflict; the quality of someone work naturally does influence collaboration decisions.)

Conflicts in science are not resolved by a strong man and presented at a press conference, rather it is a continual collective negotiation. That makes the conflicts and the influence of evidence of harassment less visible.

Two examples: If I had a preliminary result I was not sure about yet and I presented it at a conference to get feedback and thus had added a no-tweet sign to my talk. In this hypothetical case, our friend of the freedom to tweet, Gavin Schmidt, could naturally tweet about it anyway. I would then see that as a breach of trust and would only collaborate with Gavin if really no other scientist had the competencies I would need for a specific project. Gavin likely could not care less about a dwarf from an adjacent field. More important is how the scientific community sees the situation. If they see my request as reasonable, the tweet would hurt Gavin’s reputation. For something as small as this he will not lose his post as director of GISS, but people might be a little more reluctant to collaborate and less willing to share preliminary results with him lest he may tweet it. His closest friends may communicate that to Gavin in private; most will just draw their conclusions and not say anything. That makes those close friends so important, because it is better to know.

That Gavin has tweeted that he does not agree with a conference presentation of Peter Wadhams may cool their relationship. However, there was no breach of trust because Wadhams had said similar things in public before. Thus Wadhams’ unreasonable complaints about Gavin’s tweet probably only lead to further reputation losses for Wadhams.

One of the famous quotes from the stolen emails of climategate is that Phil Jones writes that he wants to keep two articles out of the IPCC report even if he has to redefine what peer reviewed means. What the mitigation sceptics normally do not say, because that does not fit their narrative, is that these two articles were referenced and discussed in the IPCC report.

Given that there are no further climategate emails on this topic I assume that Phil himself thought better of it later. In this case there is no loss of reputation; scientists are also humans, he corrected his initial error and no damage was done. If not his colleagues made clear what they thought of this and Phil decided that this was not worth a loss of reputation. If the article in question was really bad, the loss of reputation would have been limited; if the article was half-way decent the loss of reputation would have been large.

Summarizing, while visually maybe nothing seems to happen and it is difficult to fire faculty members, spreading evidence of harassment does result in reputation loss, reductions in collaboration and productivity and the ability to do the research you would like to do.

Young and female scientists

Young scientists and female scientists are also more important than they may realise. When you are young, doing science is centred around writing articles and somewhat later also on getting funding for your own (small) project. The next step in a scientific career is leading larger projects. At least in Germany and the EU, it is impossible to get funding for such projects without involving female scientists. If you lose support of (nearly) all female colleagues, your career stops right there.

The final step in a scientific career is getting your people into permanent positions. This stabilises your power and somehow professors tend to think that their topic is so important that it should grow at the expense of others. For this, and for the successful completion of scientific projects, you need to be a good talent scout and find young people you can build up.

When I studied physics in Groningen, rumour had it that there were so many professors that they were fighting to be allowed to give lectures and thus get access to the students. That way you can interest the students for your topic and assess who are the good ones. Also companies often pay for professorships to be able to search for talent and lure the good students to work for them.

Binding good students to your group with interesting bachelor and master projects and with student help positions gives professors the possibility to interest the good ones to pursue a PhD in their group. The best of those will hopefully become important scientists and grow the field.

Jim C. Hines, @jimchines

Gossip and punishing bad behaviour

When I was a PhD student, two American scientists misbehaved, I made sure that everyone knew, especially all Americans I knew. When they did it to you, they likely did it to others. Multiple similar bad stories can do real damage to a scientists reputation and their work.

Even if your case is not strong or big enough to go to the police, talk to other female scientists. There are bound to be more victims, which makes the case stronger and makes it more important that the harassment stops. Talk to other male scientists, you may be surprised how many will support you. I know of one multiple harassment case. Too small to be a legal case, but once the leaders of that community heard about it, they talked to the guy, made it clear that any future complaint would have severe consequences and I did not hear about new complaints.

Fighting back is necessary; make sure people know what happened. If bad behaviour goes unpunished it will proliferate. Economic games have shown that collaborative behaviour will only prevail if cooperative people are willing to punish bad behaviour. It also showed that people from all over the world are willing to punish bad behaviour at a cost and derive pleasure from that. Gossip and punishing bad behaviour are the two main pillars of our human civilization.

Takeaways

The media likes to portray scientists as solitary geniuses. In reality scientists need to be good collaborators to become heroes in their field. Many young scientists think that senior scientists are one big pack and may feel that it would not help to talk to senior scientists about the poor behaviour of another. But this is not true either: there are conflicts, they are just not very visible.

It would be good if (female) scientists would speak about (sexual) harassment at work and this likely does more damage than they think and see. I know actually doing this is difficult and there are unfortunately likely repercussions if you do so openly. In addition much should change in the organization of science and in society to reduce harassment. I just want to say: you have more power than you may think.

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Related reading

How to end sexual harassment in astronomy on changes in the organization that are needed.

Nature: Scientific groups revisit sexual-harassment policies. Interesting: The American Geophysical Union did not receive any harassment complaints since it started tracking them 5 years ago. It could be underreporting, I hope it means that the situation is not as bad in the geosciences.

Nature: Science and sexism: In the eye of the Twitterstorm. Social media is shaking up how scientists talk about gender issues.

Astropixy: How not to "deal with it". A example of harassment by a non-famous astronomer, makes clear that unfortunately getting rid of a faculty member is hard.

Nature Blog. The faculty series: Learning to collaborate. "Collaborations are the key to success in modern scientific research, says Michelle Ma."

The ultimatum game, a key experiment showing intrinsic fairness and altruism among strangers

*PI: Principle Investigator. The leader of a scientific project and often a group.

Anthony Watts at AGU2015: Comparison of Temperature Trends Using an Unperturbed Subset of The U.S. Historical Climatology Network

[UPDATE. I will never understand how HotWhopper writes such understandable articles so fast, but it might be best to read the HotWhopper introduction first.]

Remember the Watts et al. manuscript in 2012? Anthony Watts putting his blog on hold to urgently finish his draft? This study is now a poster at the AGU conference and Watts promises to submit it soon to an undisclosed journal.

On first sight, the study now has a higher technical quality and some problems have been solved. The two key weakness are, however, not discussed in the press release to the poster. This is strange. I have had long discussions with second author Evan Jones about this. Scientists (real sceptics) have to be critical about their own work. You would expect a scientist to focus a large part of a study on any weaknesses, if possible try to show they probably do not matter or else at least honestly confront the weaknesses, rather than simply ignore them.

Watts et al. is about the immediate surrounding, also called micro-siting, of weather stations that measure the surface air temperature. The American weather stations have been assessed for their quality in five categories by volunteers of the blog WUWT. Watts and colleagues call the two best categories "compliant" and the three worst ones "non-compliant". For these two categories they then compare the average temperature signal for the 30-year period 1979 – 2008.

An important problem of the 2012 version of this study was that historical records typically also contain temperature changes because the method of observation has changed. An important change in the USA is the time of observation bias. In the past observations were more often made in the afternoon than in the morning. Morning measurements results in somewhat lower temperatures. This change in the time of observation creates a bias of about 0.2°C per century and was ignored in the 2012 study. Also the auditor, Steve McIntyre, who was then a co-author admitted this was an error. This problem is now fixed; stations with a change in the time of observation have been removed from the study.

A Stevenson screen.

Another important observational change in the USA is the change of the screen used to protect the thermometer from the sun. In the past, so-called Cotton Region Shelters (CRS) or Stevenson screens were used, nowadays more and more automatic weather stations (AWS) are used.

A much used type of AWS in the USA is the MMTS. America was one of the first countries to automatize its network, with then analogue equipment that did not allow for long cables between the sensor and the display, which is installed inside a building. Furthermore, the technicians only had one day per station and as a consequence many of the MMTS systems were badly sited. Although they are badly sited, these MMTS system typically measure 0.2°C 0.15°C cooler temperatures. The size of the cooling has been estimated by comparing a station with such a change with a neighbouring station where nothing happens. Because both stations experience about the same weather, the difference signal shows the jump in the mean temperature more clearly.

Two weaknesses

Weakness 1 is that the authors only know the siting quality at the end of the period. Stations in the compliant categories may have been in less well sited earlier on, while stations in the non-compliant categories may have been better sited before.

pete:

Someone has a weather station in a parking lot. Noticing their error, they move the station to a field, creating a great big cooling-bias inhomogeneity. Watts comes along, and seeing the station correctly set up says: this station is sited correctly, and therefore the raw data will provide a reliable trend estimate.

The study tries to reduce this problem by creating a subset of stations that is unperturbed by Time of Observation changes, station moves, or rating changes. At least according to the station history (metadata). The problem is that metadata is never perfect.

The scientists working on homogenization thus advise to always also detect changes in the observational methods (inhomogeneities) by comparing a station to its neighbours. I have told Evan Jones how important this is, but they refuse to use homogenization methods because they feel homogenization does not work. In a scientific paper, they will have to provide evidence to explain why they reject an established method that could ameliorate a serious problem with their study. The irony is that the MMTS adjustments, which the Watts et al. study does use, depend on the same principle.

Weakness 2 is that the result is purely statistical and that no physical explanation is provided for the result. It is clear that bad micro-siting will lead to a temperature bias, but this does not affect the trend, while the study shows a difference in trend. I would not know how bad or good constant siting quality would change a trend. The press release also does not offer an explanation.

What makes this trend difference even more mysterious, if it were real, is that it mainly happens in the 1980s and 1990s, but has stopped in the last decade. See the graph below showing the trend for compliant (blue) and non-compliant stations (orange).



[UPDATE. The beginning period in which the difference builds up and that since 1996 the trends for "compliant" and "non-compliant" stations is the same is better seen in the graph below computed from the data in the above figure digitized by George Bailley. (No idea what the unit of the y-axis is on either of these graphs. Maybe 0.001°C.)


]

That the Watts phenomenon has stopped is also suggested by a comparison of the standard USA climate network (USHCN) and a new climate-quality network with perfect siting (USCRN) shown below. The pristine network even warms a little more. (Too little to be interpreted.)



While I am unable to see a natural explanation for the trend difference, that the difference is mainly seen in the first two decades fits to the hypothesis that the siting quality of the compliant stations was worse in the past: that in the past these stations were less compliant and a little too warm. The further you go back in time, the more likely it becomes that some change has happened. And the further you go back in time, the more likely it is that this change is no longer known.

six key findings

Below I have quoted the six key findings of Watts et al. (2015) according to the press release.

1. Comprehensive and detailed evaluation of station metadata, on-site station photography, satellite and aerial imaging, street level Google Earth imagery, and curator interviews have yielded a well-distributed 410 station subset of the 1218 station USHCN network that is unperturbed by Time of Observation changes, station moves, or rating changes, and a complete or mostly complete 30-year dataset. It must be emphasized that the perturbed stations dropped from the USHCN set show significantly lower trends than those retained in the sample, both for well and poorly sited station sets.

The temperature network in the USA has on average one detectable break every 15 years (and a few more breaks that are too small to be detected, but can still influence the result). The 30-year period studied should thus contain on average 2 breaks and likely only 12.6% of the stations do not have a break (154 stations). According to Watts et al. 410 of 1218 stations have no break. 256 stations (more than half their "unperturbed" dataset) thus likely have a break that Watts et al. did not find.

That the "perturbed" stations have a smaller trend than the "unperturbed" stations confirms what we know: that in the USA the inhomogeneities have a cooling bias. In the "raw" data the "unperturbed" subset has a trend in the mean temperature of 0.204°C per decade; see table below. In the "perturbed" subset the trend is only 0.126°C per decade. That is a whooping cooling difference of 0.2°C over this period.


Table 1 of Watts et al. (2015)

2. Bias at the microsite level (the immediate environment of the sensor) in the unperturbed subset of USHCN stations has a significant effect on the mean temperature (Tmean) trend. Well sited stations show significantly less warming from 1979 – 2008. These differences are significant in Tmean, and most pronounced in the minimum temperature data (Tmin). (Figure 3 and Table 1 [shown above])

The stronger trend difference for the minimum temperature would also need an explanation.

3. Equipment bias (CRS [Cotton Region Shelter] v. MMTS [Automatic Weather station] stations) in the unperturbed subset of USHCN stations has a significant effect on the mean temperature (Tmean) trend when CRS stations are compared with MMTS stations. MMTS stations show significantly less warming than CRS stations from 1979 – 2008. (Table 1 [shown above]) These differences are significant in Tmean (even after upward adjustment for MMTS conversion) and most pronounced in the maximum temperature data (Tmax).

The trend for the stations that use a Cotton Region Shelter is 0.3°C per decade. That is large and should be studied. This was the typical shelter in the past. Thus we can be quite sure that in these cases the shelter did not change, but there could naturally have been other changes.

4. The 30-year Tmean temperature trend of unperturbed, well sited stations is significantly lower than the Tmean temperature trend of NOAA/NCDC official adjusted homogenized surface temperature record for all 1218 USHCN stations.

It is natural that the trend in the raw data is smaller than the trend in the adjusted data. Mainly for the above mentioned reasons (TOBS and MMTS) the biases in the USA are large compared to the rest of the world and the trend in the USA is adjusted 0.4°C per century upwards.

5. We believe the NOAA/NCDC homogenization adjustment causes well sited stations to be adjusted upwards to match the trends of poorly sited stations.

Well, they already wrote "we believe". There is no evidence for this claim.

6. The data suggests that the divergence between well and poorly sited stations is gradual, not a result of spurious step change due to poor metadata.

The year to year variations in a single station series is about 1°C. I am not sure whether one would see whether the inhomogeneity is one or more step changes or a gradual change.

Review

If I were reviewer of this manuscript, I would ask about some choices that seem arbitrary and I would like to know whether they matter. For example using the period 1979 – 2008 and not continuing the data to 2015. It is fine to also show data until 2008 for better comparisons with earlier papers, but stopping 7 years earlier is suspicious. Also the choice to drop stations with TOBS changes, but to correct stations with MMTS changes sounds strange. It would be of interest whether any of the other 3 options show different results. Anomalies should be computed over a period, not relative to the starting year.

I hope that Anthony Watts and Evan M. Jones find the above comments useful. Jones wrote earlier this year:

Oh, a shout-out to Dr. Venema, one of the earlier critics of Watts et al. (2012) who pointed out to us things that needed to be accounted for, such as TOBS, a stricter hand on station moves, and MMTS equipment conversion.

Note to Anthony: In terms of reasonable discussion, VV is way up there. He actually has helped to point us in a better direction. I think both Victor Venema and William Connolley should get a hat-tip in the paper (if they would accept it!) because their well considered criticism was of such great help to us over the months since the 2012 release. It was just the way science is supposed to be, like you read about in books.

Watts wrote in the side notes to his press release:

Even input from openly hostile professional people, such as Victor Venema, have been highly useful, and I thank him for it.

Glad to have been of help. I do not recall having been "openly hostile" to this study. It would be hard to come to a positive judgement of the quality of the blog posts at WUWT, whether they are from the pathological misquoter Monckton or greenhouse effect denier Tim Ball.

However, it is always great when people contribute to the scientific literature. When the quality of their work meets the scientific standard, it does not matter what their motivation is, then science can learn something. The surface stations project is useful to learn more about the quality of the measurements; also for trend studies if continued over the coming decades.

Comparison of Temperature Trends Using an Unperturbed Subset of The U.S. Historical Climatology Network

Anthony Watts, Evan Jones, John Nielsen-Gammon and John Christy

Abstract. Climate observations are affected by variations in land use and land cover at all scales, including the microscale. A 410-station subset of U.S. Historical Climatology Network (version 2.5) stations is identified that experienced no changes in time of observation or station moves during the 1979-2008 period. These stations are classified based on proximity to artificial surfaces, buildings, and other such objects with unnatural thermal mass using guidelines established by Leroy (2010). The relatively few stations in the classes with minimal artificial impact are found to have raw temperature trends that are collectively about 2/3 as large as stations in the classes with greater expected artificial impact. The trend differences are largest for minimum temperatures and are statistically significant even at the regional scale and across different types of instrumentation and degrees of urbanization. The homogeneity adjustments applied by the National Centers for Environmental Information (formerly the National Climatic Data Center) greatly reduce those differences but produce trends that are more consistent with the stations with greater expected artificial impact. Trend differences between the Cooperative Observer Network and the Climate Reference Network are not found during the 2005-2014 sub-period of relatively stable temperatures, suggesting that the observed differences are caused by a physical mechanism that is directly or indirectly caused by changing temperatures.

[UPDATE. I forgot to mention the obvious: After homogenization the trend Watts et al. (2015) computed are nearly the same for all five siting categories, just like it was for Watts et al. (2012) and the published study Fall et al. Thus for the data used by climatologists, the homogenized data, the siting quality does not matter. Just like before, they did not study homogenization algorithms and thus cannot draw any conclusions about them, but unfortunately they do.]

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Related reading

Anthony Watts' #AGU15 poster on US temperature trends

Blog review of the Watts et al. (2012) manuscript on surface temperature trends

A short introduction to the time of observation bias and its correction

Comparing the United States COOP stations with the US Climate Reference Network

WUWT not interested in my slanted opinion

Some history from 2010

On Weather Stations and Climate Trends

The conservative family values of Christian man Anthony Watts

Watts not to love: New study finds the poor weather stations tend to have a slight COOL bias, not a warm one

Poorly sited U.S. temperature instruments not responsible for artificial warming

My theoretical #AGU15 program

This Monday the Fall Meeting of the American Geophysical Union (AGU2015) starts. An important function of conferences is knowing who is working on what. Browsing the program does a lot of that, even if you are not there. Here is an overview of where I would have had a look from my interest in climate data quality.

Links, emphasis and [explanations] are mine. The titles are linked to the AGU abstracts, where you can also mail, tweet or facebook the abstracts to others who may be interested.

Session: Taking the Temperature of the Earth: Long-Term Trends and Variability across All Domains of Earth's Surface

(Talks | Posters)

Inland Water Temperature and the recent Global Warming Hiatus

Simon J Hook, Nathan Healey, John D Lenters and Catherine O'Reilly

Extract abstract. We are using thermal infrared satellite data in conjunction with in situ measurements to produce water temperatures for all the large inland water bodies in North America and the rest of the world for potential use as climate indicator. Recent studies have revealed significant warming of inland waters throughout the world. The observed rate of warming is – in many cases – greater than that of the ambient air temperature. These rapid, unprecedented changes in inland water temperatures have profound implications for lake hydrodynamics, productivity, and biotic communities. Scientists are just beginning to understand the global extent, regional patterns, physical mechanisms, and ecological consequences of lake warming.

See also my previous post on the fast warming of rivers and lakes and the decrease in their freezing periods. Unfortunately the abstract does not say much about the "hiatus" mentioned in the title.

There is also a detailed study on the relationship between air and water temperature for Lake Tahoe.

Global near-surface temperature estimation using statistical reconstruction techniques

Colin P Morice, Nick A Rayner and John Kennedy

abstract. Incomplete and non-uniform observational coverage of the globe is a prominent source of uncertainty in instrumental records of global near-surface temperature change. In this study the capabilities of a range of statistical analysis methods are assessed in producing improved estimates of global near-surface temperature change since the mid 19th century for observational coverage in the HadCRUT4 data set. Methods used include those that interpolate according to local correlation structure (kriging) and reduced space methods that learn large-scale temperature patterns.

The performance of each method in estimating regional and global temperature changes has been benchmarked in application to a subset of CMIP5 simulations. Model fields are sub-sampled and simulated observational errors added to emulate observational data, permitting assessment of temperature field reconstruction algorithms in controlled tests in which globally complete temperature fields are known.

The reconstruction methods have also been applied to the HadCRUT4 data set, yielding a range of estimates of global near-surface temperature change since the mid 19th century. Results show relatively increased warming in the global average over the 21st century owing to reconstruction of temperatures in high northern latitudes, supporting the findings of Cowtan & Way (2014) and Karl et al. (2015). While there is broad agreement between estimates of global and hemispheric changes throughout much of the 20th and 21st century, agreement is reduced in the 19th and early 20th century. This finding is supported by the climate model trials that highlight uncertainty in reconstructing data sparse regions, most notably in the Southern Hemisphere in the 19th century. These results underline the importance of continued data rescue activities, such as those of the International Surface Temperature Initiative and ACRE.

The results of this study will form an addition to the HadCRUT4 global near-surface temperature data set.

The EUSTACE project: delivering global, daily information on surface air temperature

Nick A Rayner and Colin P Morice,
At first sight you may think my colleagues have gone crazy. A daily spatially complete global centennial high-resolution temperature dataset!

I would be so happy if we could get halfway reliable estimates of changes in weather variability and extremes from some high-quality, high-density station networks for the recent decades. It is really hard to detect and remove changes in variability due to changes in the monitoring practices and these changes are most likely huge.

However, if you read carefully, they only promise to make the dataset and do not promise that the data is fit for any specific use. One of the main ways mitigation sceptics misinform the public is by pretending that datasets provide reliable information for any application. In reality the reliability of a feature needs to be studied first. Let's be optimistic and see how far they will get; they just started, have a nice bag of tricks and mathematical prowess.

Day-to-day variations in surface air temperature affect society in many ways; however, daily surface air temperature measurements are not available everywhere. A global daily analysis cannot be achieved with measurements made in situ alone, so incorporation of satellite retrievals is needed. To achieve this, we must develop an understanding of the relationships between traditional (land and marine) surface air temperature measurements and retrievals of surface skin temperature from satellite measurements, i.e. Land Surface Temperature, Ice Surface Temperature, Sea Surface Temperature and Lake Surface Water Temperature. These relationships can be derived either empirically or with the help of a physical model.

Here we discuss the science needed to produce a fully-global daily analysis (or ensemble of analyses) of surface air temperature on the centennial scale, integrating different ground-based and satellite-borne data types. Information contained in the satellite retrievals would be used to create globally-complete fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place. ...

A separate poster will provide more details on the satellite data.

The International Surface Temperature Initiative

Peter Thorne, Jay H Lawrimore, Kate Willett and Victor Venema

The Initiative is a multi-disciplinary effort to improve our observational understanding of all relevant aspects of land surface air temperatures from the global-mean centennial scale trends to local information relevant for climate services and climate smart decisions. The initiative was started in 2010 with a meeting that set the overall remit and direction. In the intervening 5 years much progress has been made, although much remains to be done. This talk shall highlight: the over-arching initiative framework, some of the achievements and major outcomes to date, as well as opportunities to get involved. It shall also highlight the many challenges yet to be addressed as we move from questions of global long-term trends to more local and regional data requirements to meet emerging scientific and societal needs.

Methodologies and Resulting Uncertainties in Long-Term Records of Ozone and Other Atmospheric Essential Climate Variables Constructed from Multiple Data Sources

Trends in atmospheric temperature and winds since 1959

Steven C Sherwood, Nidhi Nishant and Paul O'Gorman

Sherwood and colleagues have generated a new radiosonde dataset, removing artificial instrumental changes as well as they could. They find that the tropical hotspot does exist, that the models predictions of this tropic hotspot in the tropical tropospheric trends thus fit. They find that the recent tropospheric trend is not smaller than before. I hope there is no Australian Lamar Smith who needs an "hiatus" and is willing to harass scientists for political gain.

That there is not even a non-significant change from the long-term warming trend is surprising because of the recently more frequent cooling El Nino Southern Oscillation (ENSO) phases (La Nina phases). One would expect to see the influence of ENSO even stronger in the tropospheric temperatures than in the 2-m temperature. This makes it more likely that this insignificant trend change in the 2-m temperature is a measurement artefact.

Extract abstract.We present an updated version of the radiosonde dataset homogenized by Iterative Universal Kriging (IUKv2), now extended through February 2013, following the method used in the original version (Sherwood et al 2008 Robust tropospheric warming revealed by iteratively homogenized radiosonde data J. Clim. 21 5336–52). ...

Temperature trends in the updated data show three noteworthy features. First, tropical warming is equally strong over both the 1959–2012 and 1979–2012 periods, increasing smoothly and almost moist-adiabatically from the surface (where it is roughly 0.14 K/decade) to 300 hPa (where it is about 0.25 K/decade over both periods), a pattern very close to that in climate model predictions. This contradicts suggestions that atmospheric warming has slowed in recent decades or that it has not kept up with that at the surface. ...

Wind trends over the period 1979–2012 confirm a strengthening, lifting and poleward shift of both subtropical westerly jets; the Northern one shows more displacement and the southern more intensification, but these details appear sensitive to the time period analysed. Winds over the Southern Ocean have intensified with a downward extension from the stratosphere to troposphere visible from austral summer through autumn. There is also a trend toward more easterly winds in the middle and upper troposphere of the deep tropics, which may be associated with tropical expansion.

Uncertainty in Long-Term Atmospheric Data Records from MSU and AMSU

In session: Methodologies and Resulting Uncertainties in Long-Term Records of Ozone and Other Atmospheric Essential Climate Variables Constructed from Multiple Data Sources
Carl Mears

This talk presents an uncertainty analysis of known errors in tropospheric satellite temperature changes and an ensemble of possible estimates that makes computing uncertainties for a specific application easier.

The temperature of the Earth’s atmosphere has been continuously observed by satellite-borne microwave sounders since late 1978. These measurements, made by the Microwave Sounding Units (MSUs) and the Advanced Microwave Sounding Units (AMSUs) yield one of the longest truly global records of Earth’s climate. To be useful for climate studies, measurements made by different satellites and satellite systems need to be merged into a single long-term dataset. Before and during the merging process, a number of adjustments made to the satellite measurements. These adjustments are intended to account for issues such as calibration drifts or changes in local measurement time. Because the adjustments are made with imperfect knowledge, they are therefore not likely to reduce errors to zero, and thus introduce uncertainty into the resulting long-term data record. In this presentation, we will discuss a Monte-Carlo-based approach to calculating and describing the effects of these uncertainty sources on the final merged dataset. The result of our uncertainty analysis is an ensemble of possible datasets, with the applied adjustments varied within reasonable bounds, and other error sources such as sampling noise taken into account. The ensemble approach makes it easy for the user community to assess the effects of uncertainty on their work by simply repeating their analysis for each ensemble member.

Other sessions

The statistical inhomogeneity of surface air temperature in global atmospheric reanalyses

In session: Evaluating Reanalysis: What Can We Learn about Past Weather and Climate? (Talks I | Talks II | posters)
Craig R Ferguson and Min-Hee Lee

Recently, a new generation of so-called climate reanalyses has emerged, including the 161-year NOAA—Cooperative Institute for Research in Environmental Sciences (NOAA-CIRES) Twentieth Century Reanalysis Version 2c (20CR V2c), the 111-year ECMWF pilot reanalysis of the twentieth century (ERA-20C), and the 55-year JMA conventional reanalysis (JRA-55C). These reanalyses were explicitly designed to achieve improved homogeneity through assimilation of a fixed subset of (mostly surface) observations. We apply structural breakpoint analysis to evaluate inhomogeneity of the surface air temperature in these reanalyses (1851-2011). For the modern satellite era (1979-2013), we intercompare their inhomogeneity to that of all eleven available satellite reanalyses. Where possible, we distinguish between breakpoints that are likely linked to climate variability and those that are likely due to an artificial observational network shift. ERA-20C is found to be the most homogeneous reanalysis, with 40% fewer artificial breaks than 20CR V2c. Despite its gains in homogeneity, continued improvements to ERA-20C are needed. In this presentation, we highlight the most spatially extensive artificial break events in ERA-20C.

There is also a more detailed talk about the quality of humidity in reanalysis over China.

Assessment of Precipitation Trends over Europe by Comparing ERA-20C with a New Homogenized Observational GPCC Dataset

In session: Evaluating Reanalysis: What Can We Learn about Past Weather and Climate?
Elke Rustemeier, Markus Ziese, Anja Meyer-Christoffer, Peter Finger, Udo Schneider and Andreas Becker

...The monthly totals of the ERA-20C reanalysis are compared to two corresponding Global Precipitation Climatology Centre (GPCC) products; the Full Data Reanalysis Version 7 and the new HOMogenized PRecipitation Analysis of European in-situ data (HOMPRA Europe).
ERA-20C...covers the time period 1900 to 2010. Only surface observations are assimilated namely marine winds and pressure. This allows the comparison with independent, not assimilated data.

Sounds interesting, the abstract unfortunately does not give much results yet.

Cyclone Center: Insights on Historical Tropical Cyclones from Citizen Volunteers

In session: Era of Citizen Science and Big Data: Intersection of Outreach, Crowd-Sourced Data, and Scientific Research
Peter Thorne, Christopher Hennon, Kenneth Knapp, Carl Schreck, Scott Stevens, James Kossin, Jared Rennie, Paula Hennon, Michael Kruk

The cyclonecenter.org project started in fall 2012 and has been collecting citizen scientist volunteer tropical cyclone intensity estimates ever since. The project is hosted by the Citizen Science Alliance (zooniverse) and the platform is supported by a range of scientists. We have over 30 years of satellite imagery of tropical cyclones but the analysis to date has been done on an ocean-basin by ocean-basin basis and worse still practices have changed over time. We therefore do not, presently, have a homogeneous record relevant for discerning climatic changes. Automated techniques can classify many of the images but have a propensity to be challenged during storm transitions. The problem is fundamentally one where many pairs of eyes are invaluable as there is no substitute for human eyes in discerning patterns. Each image is classified by ten unique users before it is retired. This provides a unique insight into the uncertainty inherent in classification. In the three years of the project much useful data has accrued. This presentation shall highlight some of the results and analyses to date and touch on insights as to what has worked and what perhaps has not worked so well. There are still many images left to complete so its far from too late to jump over to www.cyclonecenter.org and help out.

Synergetic Use of Crowdsourcing for Environmental Science Research, Applications and Education

In session: Era of Citizen Science and Big Data: Intersection of Outreach, Crowd-Sourced Data, and Scientific Research
Udaysankar Nair

...Contextual information needed to effectively utilize the data is sparse. Examples of such contextual information include ground truth data for land cover classification, presence/absence of species, prevalence of mosquito breeding sites and characteristics of urban land cover. Often, there are no agencies tasked with routine collection of such contextual information, which could be effectively collected through crowdsourcing.

Crowdsourcing of such information, that is useful for environmental science research and applications, also provide opportunities for experiential learning at all levels of education. Appropriately designed crowdsourcing activity can be transform students from passive recipients of information to generators of knowledge. ... One example is crowdsourcing of land use and land cover (LULC) data using Open Data Kit (ODK) and associated analysis of satellite imagery using Google Earth Engine (GEE). Implementation of this activity as inquiry based learning exercise, for both middle school and for pre-service teachers will be discussed. Another example will detail the synergy between crowdsourcing for biodiversity mapping in southern India and environmental education...

There is also a crowd sourced project for Land use and land cover for (urban) climatology: the World Urban Database. A great initiative.

Steps Towards a Homogenized Sub-Monthly Temperature Monitoring Tool

In session: Characterizing and Interpreting Changes in Temperature and Precipitation Extremes
Jared Rennie and Kenneth Kunkel

Land surface air temperature products have been essential for monitoring the evolution of the climate system. Before a temperature dataset is included in such reports, it is important that non-climatic influences be removed or changed so the dataset is considered homogenous. These inhomogeneities include changes in station location, instrumentation and observing practices. Very few datasets are free of these influences and therefore require homogenization schemes. While many homogenized products exist on the monthly time scale, few daily products exist... Using these datasets already in existence, monthly adjustments are applied to daily data [of NOAA's Global Historical Climatology Network – Daily (GHCN-D) dataset]...

Great to see NOAA make first steps towards homogenization of their large daily data collection, a huge and important task.

Note that the daily data is only adjusted for changes in the monthly means. This is an improvement, but for the weather extremes, the topic of this session, also the rest of the marginal distribution needs to be homogenized.

Temperature Trends over Germany from Homogenized Radiosonde Data

In session: Methodologies and Resulting Uncertainties in Long-Term Records of Ozone and Other Atmospheric Essential Climate Variables Constructed from Multiple Data Sources
Wolfgang Steinbrecht and Margit Pattantyús Ábráham

We present homogenization procedure and results for Germany’s historical radiosonde [(RS)] records, dating back to the 1950s. Our manual homogenization makes use of the different RS networks existing in East and West-Germany from the 1950s until 1990.

The largest temperature adjustments, up to 2.5K, are applied to Freiberg sondes used in the East in the 1950s and 1960s. Adjustments for Graw H50 and M60 sondes, used in the West from the 1950s to the late 1980s, and for RKZ sondes, used in the East in the 1970s and 1980s, are also significant, 0.3 to 0.5K. Small differences between Vaisala RS80 and RS92 sondes used throughout Germany since 1990 and 2005, respectively, were not corrected for at levels from the ground to 300 hPa.

Comparison of the homogenized data with other radiosonde datasets, RICH (Haimberger et al., 2012) and HadAT2 (McCarthy et al., 2008), and with Microwave Sounding Unit satellite data (Mears and Wentz, 2009), shows generally good agreement. HadAT2 data exhibit a few suspicious spikes in the 1970s and 1980s, and some suspicious offsets up to 1K after 1995. Compared to RICH, our homogenized data show slightly different temperatures in the 1960s and 1970s. We find that the troposphere over Germany has been warming by 0.25 ± 0.1K per decade since the early 1960s, slightly more than reported in other studies (Hartmann et al., 2013). The stratosphere has been cooling, with the trend increasing from almost no change near 230hPa (the tropopause) to -0.5 ± 0.2K per decade near 50hPa. Trends from the homogenized data are more positive by about 0.1K per decade compared to the original data, both in troposphere and stratosphere.

Statistical relative homogenization can only partially remove trend biases. Given that the trend needed to be corrected upwards, the real temperature trend may thus be larger.

Observed Decrease of North American Winter Temperature Variability

In session: Methodologies and Resulting Uncertainties in Long-Term Records of Ozone and Other Atmospheric Essential Climate Variables Constructed from Multiple Data Sources
Andrew Rhines, Martin Tingley, Karen McKinnon, Peter Huybers

There is considerable interest in determining whether temperature variability has changed in recent decades. Model ensembles project that extratropical land temperature variance will detectably decrease by 2070. We use quantile regression of station observations to show that decreasing variability is already robustly detectable for North American winter during 1979--2014. ...

We find that variability of daily temperatures, as measured by the difference between the 95th and 5th percentiles, has decreased markedly in winter for both daily minima and maxima. ... The reduced spread of winter temperatures primarily results from Arctic amplification decreasing the meridional temperature gradient. Greater observed warming in the 5th relative to the 95th percentile stems from asymmetric effects of advection [air movements] during cold versus warm days; cold air advection is generally from northerly regions that have experienced greater warming than western or southwestern regions that are generally sourced during warm days.

Studies on changes in variability are the best. Guest appearances of the Arctic polar vortex in the eastern USA had given me the impression that the variability had increased, not decreased. Interesting.

Century-Scale of Standard Deviation in Europe Historical Temperature Records

In session: Methodologies and Resulting Uncertainties in Long-Term Records of Ozone and Other Atmospheric Essential Climate Variables Constructed from Multiple Data Sources
Fenghua Xie

The standard deviation (STD) variability in long historical temperature records in Europe is analyzed. It is found that STD is changeable with time, and a century-scale variation is revealed, which further indicates a century-scale intensity modulation of the large-scale temperature variability.

The Atlantic multidecadal oscillation (AMO) can cause significant impacts in standard deviation. During the periods of 1870–1910 and 1950–80, increasing standard deviation corresponds to increasing AMO index, while the periods of 1920-50 and 1980-2000 decreasing standard deviation corresponds to decreasing AMO index. The findings herein suggest a new perspective on the understanding of climatic change

Studies on changes in variability are the best. This intriguing osculation in the standard deviation of temperature was found before for the Greater Alpine Region by Reinhard Böhm. He also found it in precipitation and pressure (with a little fantasy). One be careful with such studies, changes in the standard deviation due to changes in monitoring practises (inhomogeneities) are mostly not detected, nor corrected. Only a few national and regional daily datasets have been (partially) homogenized in this respect.

Could scientists ‘peer-review’ the web?

A Town Hall presentation by Emmanuel Vincent of Climate Feedback and Dan Whaley, the founder of Hypothesis, which is the software basis of Climate Feedback. On Tuesday Dec.15, 12:30 - 13:30 pm

It is quite sure that I missed relevant presentations. Please add them in the comments.

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