New paper finds 2009-2010 winter was colder than 1783-1784 in France

A paper in open review for Climate of the Past finds no significant difference between the atmospheric variables of sea-level pressure, surface temperature and wind speed between the winters of 1783-1784 [during the Little Ice Age] and 2009-2010 in Europe, and finds the 2009-2010 winter in France was colder than 1783-1784, in spite of a large volcanic eruption that occurred in 1783.



Excerpt: 

"The results on temperature reconstructions are coherent with historical and proxy records in Europe, especially during the Laki eruption period. D’Arrigo et al. (2011) suggested that the winter conditions in 1783/1784 resembled those of the 2009/2010 winter. Our analysis confirms this inference, although the 1783/1784 winter was not as cold as 2009/2010, at least in France."

Clim. Past Discuss., 9, 5157-5182, 2013 www.clim-past-discuss.net/9/5157/2013/ doi:10.5194/cpd-9-5157-2013

Ensemble meteorological reconstruction using circulation analogues of 1781–1785

P. Yiou¹, M. Boichu², R. Vautard¹, M. Vrac¹, S. Jourdain³, E. Garnier⁴, F. Fluteau⁵, and L. Menut<sup>2

1</sup>Laboratoire des Sciences du Climat et de l'Environnement, UMR8212 CEA-CNRS-UVSQ & IPSL, CE Saclay l'Orme des Merisiers, 91191 Gif-sur-Yvette, France
²Laboratoire de Météorologie Dynamique, UMR8539 X-ENS-UPMC & IPSL, Ecole Polytechnique, 91128 Palaiseau, France
³DClim, Météo France, 42 Avenue G. Coriolis, 31057 Toulouse, France
⁴Centre de Recherche d'Histoire Quantitative, UMR6583 Université de Caen–CNRS, 14032 Caen, France
⁵Institut de Physique du Globe de Paris, 1 rue Jussieu, 75238 Paris, France

Abstract. This paper uses a method of atmospheric flow analogues to reconstruct an ensemble of atmospheric variables (namely sea-level pressure, surface temperature and wind speed) between 1781 and 1785. The properties of this ensemble are investigated and tested against observations of temperature. The goal of the paper is to assess whether the atmospheric circulation during the Laki volcanic eruption (in 1783) and the subsequent winter were similar to the conditions that prevailed in the winter 2009/2010 and during spring 2010. We find that the three months following the Laki eruption in June 1783 barely have analogues in 2010. The cold winter of 1783/1784 yields circulation analogues in 2009/2010. Our analysis suggests that it is unlikely that the Laki eruption was responsible for the cold winter of 1783/1784, of the relatively short memory of the atmospheric circulation.

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Climate scam artists now altering Arctic sea ice data to look alarming

First, they altered the temperature record to look more alarming by adjusting pre-satellite era temperatures down and post-satellite era temperatures up:

The Goddard institute for Space Studies caught red handed. Temps Adjustment DOWN pre sat era, UP since -Joe Bastardi [& Steven Goddard] H/T Tom Nelson

Now, just as Arctic Sea ice is about to hit the minimum, they've altered the sea ice extent satellite record to look more alarming by significantly lowering the minimum. For double shame!



September 6, 2013 Reblogged from Sunshine Hours:

Jaxa Version 2 – Make The Low Even Lower (and the Great Big Con continues)






Jaxa is one of the other sea ice monitoring agencies. They just came out with a version 2 of their data.

I thought … why not graph the difference between version 1 and version 2 for 2013.

Red = Version 2 Lower Than Version 1

Blue = Version 2 Higher Than Version 1

Guess what the following graph shows? It makes the minimum dramatically lower (400,000 sq km lower) and the maximum higher so the minimum looks even worse when graphed.

For shame.

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2013 Accumulated Cyclone Energy [ACE] is only 25% of normal; another 10 hurricane-free days will set a record

Accumulated Cyclone Energy has been decreasing since the mid-90's

Tropical storm Gabrielle fizzles: Why has hurricane season been so calm? (+video)

Tropical storm Gabrielle was the seventh Atlantic tropical cyclone this season, but no hurricanes have yet formed, which is unusual. Another 10 hurricane-free days would set a record.

By Pete Spotts, Staff writer / September 5, 2013




This satellite image shows tropical storm Gabrielle moving east toward the Dominican Republic Wednesday. The storm has since been downgraded to a tropical depression.

Forecasters at the National Hurricane Center in Miami have lifted tropical-storm watches and warnings they had issued for Puerto Rico. The government of the Dominican Republic has done likewise for areas along its coast that would have been affected.

Forecasters note that Gabrielle encountered one of the banes of tropical cyclones: wind shear, a sudden change in wind speed or direction with altitude. The mountains of Puerto Rico and the Dominican Republic also disrupted the storm, preventing it from becoming more organized.

While forecasters hold out the possibility that the storm could reorganize and strengthen, the official forecast calls for Gabrielle to fully fizzle out within the next 36 hours.

And so goes the 2013 Atlantic hurricane season so far.

The season has produced seven named storms to date – meaning tropical storms or hurricanes. Typically, the seventh named storm doesn't appear until Sept. 14, according to data gathered by the National Hurricane Center.

The number of named storms is on pace to fall within the range several seasonal forecasts have projected. On average, however, the season should have seen its first hurricane by now, and none has emerged. Indeed, the first major hurricane, with maximum sustained winds of 111 miles an hour or more, typically appears around Sept. 4, notes Dennis Feltgen, spokesman for the National Hurricane Center.

If the first hurricane fails to appear until after 8 a.m. Eastern Daylight Time Sept. 15, this will be the most hurricane-free first half of a season since satellites began tracking the storms in 1967, he notes in an e-mail.

One measure of a season's oomph is known as the Accumulated Cyclone Energy – a gauge of the energy tropical cyclones expend one by one and accumulated over a season. Through Sept. 5, this ACE index has reached only 25 percent of the 1981-2010 average, according to the National Hurricane Center data.

Over at the Weather Underground, Jeff Masters, the site's director of meteorology, notes that during the satellite era, only five seasons had comparably low ACE numbers. The energy expended by tropical cyclones fell off during those years either because the ocean temperatures in the main region where the storms first develop was colder than normal or because the seasons fell into El Niño years.

El Niño is part of a cyclical climate pattern in the tropical Pacific, but its extended effects include boosting wind shear in where tropical storms tend to develop in the Atlantic.

This year, El Niño is nowhere to be found and sea-surface temperatures in the main development region for Atlantic tropical cyclones have been running above normal.

Dr. Masters and others look to Africa to help explain the low-intensity season so far. Storms that have developed have been quenched by hot, dry air coming off of the Sahara.

A feature known as the Madden-Julian oscillation also has been at work, meteorologists say. Unlike El Niño and its sibling La Niña, which stick to the Pacific, the Madden-Julian oscillation is well traveled. Its hallmark: broad regions of intense tropical rainfall separated by broad regions of weak rainfall moving in trainlike fashion eastward around the world along the tropics. From the standpoint of someone on the ground watching the pattern, it brings alternating periods of intense and suppressed rainfall that can last from 30 to 60 days.

The periods of heavier rainfall in the main development region for Atlantic storms can encourage tropical-cyclone formation, while the drier phase of the oscillation can suppress tropical formation or inhibit the intensification of existing storms.

At the end of August, Masters notes, the Madden-Julian oscillation train was bringing wetter, more unstable conditions to the region where Atlantic tropical cyclones develop, which could result in greater tropical-storm formation going forward.

For people affected by the storms that have occurred, the season can't be over soon enough. Three tropical storms have struck Mexico on or around the Yucatan peninsula so far this year, and a tropical wave – precursor to a tropical depression – is currently drenching the area. The first three storms killed 14 people.

In June, tropical storm Andrea made landfall along Florida's Gulf Coast and brought much needed rain to the Southeast before it traveled up the Eastern Seaboard.

The peak of the Atlantic hurricane season runs through mid-October, so there's plenty of time for activity to amp up. But if Oct. 8 comes and goes with no hurricanes to date, that will represent a record. The latest "first" hurricane to form in an Atlantic season formed on that date in 1905.

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Washington Times: The globe cools, and Al Gore’s ‘Climate Reality’ does too

Reality intrudes on a hot dream

The globe cools, and Al Gore’s ‘Climate Reality’ does, too

The Washington Times 9/5/13

Al Gore's multimillion-dollar scheme to persuade the world that global warming is about to boil, fry or saute us all is disappearing faster than an ice cube on the sidewalk on a summer day.

The former vice president reached the peak of his popularity with the 2006 release of his Oscar-winning scare-film "An Inconvenient Truth," for which he was hailed as a genius and basked in A-list status at Hollywood soirees. He wasted no time on his return to the spotlight and founded the Climate Reality Project, a group to spread the word about an imminent planetary cataclysm that could only be averted by adopting his agenda. That's the scheme melting now.

Al collected $87.4 million in donations in 2008, and the website BuzzFeed reports that once it caught fire, he could spend nearly $30 million annually on television commercials and grass-roots operations. It even conned some conservatives, such as former House Speaker Newt Gingrich and the Rev. Pat Robertson, to appear in television commercials. Mr. Gingrich, sadder but wiser, later called the commercial the "dumbest thing I've done in the last four years."

In hindsight, perhaps the Climate Reality Project wasn't such a scorcher of an idea after all. By 2011, receipts had fallen 80 percent, to $17.6 million. Even wealthy liberals aren't terminally stupid. Wallets and purses snapped shut as reality arrived and the promised cataclysmic warming never happened.

The scorekeepers of global-warming alarmism, the U.N. Intergovernmental Panel On Climate Change, is about to release its fifth Assessment Report, which is said to admit that the planet has been cooling, not warming. A leaked draft version of the report concedes the very inconvenient truth, and casts doubt on the claim that man plays a role in triggering "extreme weather."

The U.N. forgot to send an advance copy of the findings to Mr. Gore. On Thursday, the Nobel Peace Prize winner pointed to dramatic photographs of wildfires in Yosemite National Park, as if the arid Western region had never burned prior to the Industrial Revolution. "As temps rise," Mr. Gore tweeted, "fires are becoming worse and worse across our country."

 Actually, the fires are doing no such thing.

This wildfire season has been the weakest in at least a decade, according to statistics provided by the National Fire Information Center. There were 83,919 blazes in the first nine months of the year that Mr. Gore was the toast of Tinseltown. So far this year, there have only been 35,566 fires, down dramatically from the usual. The total number of scorched acres is down 38 percent from the 10-year average of 6.2 million acres.

Facts aren't likely to deter Mr. Gore's diehard fans. They're the loyal sort who will stand up and say he did, so, invent the Internet, and he really was the inspiration for the insipid movie "Love Story." The groupies will keep mailing in the donations, and so will the crony-capitalist "green" companies, which would profit handsomely if Mr. Gore's dreams and schemes should become actual policy. The cash will keep the incandescent lights on in the Gore house of many mansions, but it won't be enough to restore his credibility. Since the planet hasn't been warming at all, it's only a matter of time before the public catches on and good ol' Al and his schemes will be put permanently on ice.

Read more: http://www.washingtontimes.com/news/2013/sep/5/editorial-reality-intrudes-on-a-hot-dream/#ixzz2e4CXLmXm Follow us: @washtimes on Twitter



H/T Junk Science

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Gore's Climate Reality Project says Gore & climate change are "one of the favorite piñatas for media and easy targets for headlines. Always have been and always will be"

According to an internal email from Gore's own Climate 'Reality' Project, "As many of you already know, AG [Al Gore] and climate change are one of the favorite piñatas for media and easy targets for headlines. Always have been and always will be."

Boo hoo hoo

Gore's false claims at left

Internal email by Dan Stiles, the Climate Reality Project’s chief operating officer and the main spokesman for the group:

Subject: Buzzfeed and the piñata

Good morning/mid-day,

I am sure many of you have read the article Buzz Feed published today about our fearless leader and our fearless non-traditional efforts. I thought I would share a few thoughts that, of course, did not make it into the article.

As many of you already know, AG [Al Gore] and climate change are one of the favorite piñatas for media and easy targets for headlines. Always have been and always will be. In that stead, this particular piece was written with a narrative that the reporter had no interest in changing despite both GPG’s and my efforts to educate them on the great work of this team. You would think Buzzfeed, of all places, would understand our approach to tackle this issue. But, alas, headlines are more important to and easier for them than writing about innovation and a community effort.

AG and Maggie have both read the article and are unwavering in their support of each of you and our strategy. As are the Climate Leaders and others who have weighed in the comment section to the article already.

...

I can’t think of anything more inspiring than the opportunity to prove a naysayer wrong. And, I hope you find that inspiring too as we head into an action packed fall.

Let’s get out there and show them how its done!

Dan

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New paper in Nature Climate Change says IPCC uses statistical techniques 'out of date by well over a decade'

A new paper published in Nature Climate Change finds, "Use of state-of-the-art statistical methods could substantially improve the quantification of uncertainty in [IPCC] assessments of climate change" and that "The forthcoming Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and the US National Climate Assessment Report will not adequately address this issue. Worse still, prevailing techniques for quantifying the uncertainties that are inherent in observed climate trends and projections of climate change are out of date by well over a decade. Modern statistical methods and models could improve this situation dramatically." The authors recommend, "Including at least one author with expertise in uncertainty analysis on all chapters of IPCC and US national assessments" and that the IPCC "Replace qualitative assessments of uncertainty with quantitative ones." A prime example of this would be the ludicrous IPCC claim that it is "very likely" man is the cause of climate change, a claim that very likely would not hold up if subject to modern statistical analysis of uncertainty.

Box 1: Recommendations to improve uncertainty quantification.

• Replace qualitative assessments of uncertainty with quantitative ones.


• Reduce uncertainties in trend estimates for climate observations and projections through use of modern statistical methods for spatio-temporal data.


• Increase the accuracy with which the climate is monitored by combining various sources of information in hierarchical statistical models.


• Reduce uncertainties in climate change projections by applying experimental design to make more efficient use of computational resources.


• Quantify changes in the likelihood of extreme weather events in a manner that is more useful to decision-makers by using methods that are based on the statistical theory of extreme values.


• Include at least one author with expertise in uncertainty analysis on all chapters of IPCC and US national assessments.

Uncertainty analysis in climate change assessments

• Richard W. Katz,


• Peter F. Craigmile,


• Peter Guttorp,


• Murali Haran,


• Bruno Sansó


• & Michael L. Stein

• Affiliations


• Corresponding author

Nature Climate Change

 

3,

 

769–771

 

(2013)

 

doi:10.1038/nclimate1980

Published online

 

28 August 2013

Article tools

Use of state-of-the-art statistical methods could substantially improve the quantification of uncertainty in assessments of climate change.

Because the climate system is so complex, involving nonlinear coupling of the atmosphere and ocean, there will always be uncertainties in assessments and projections of climate change. This makes it hard to predict how the intensity of tropical cyclones will change as the climate warms, the rate of sea-level rise over the next century or the prevalence and severity of future droughts and floods, to give just a few well-known examples. Indeed, much of the disagreement about the policy implications of climate change revolves around a lack of certainty. The forthcoming Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and the US National Climate Assessment Report will not adequately address this issue. Worse still, prevailing techniques for quantifying the uncertainties that are inherent in observed climate trends and projections of climate change are out of date by well over a decade. Modern statistical methods and models could improve this situation dramatically.

Uncertainty quantification is a critical component in the description and attribution of climate change. In some circumstances, uncertainty can increase when previously neglected sources of uncertainty are recognized and accounted for (Fig. 1 shows how uncertainty can increase for projections of sea-level rise). In other circumstances, more rigorous quantification may result in a decrease in the apparent level of uncertainty, in part because of more efficient use of the available information. For example, despite much effort over recent decades, the uncertainty in the estimated climate sensitivity (that is, the long-term response of global mean temperature to a doubling of the CO2 concentration in the atmosphere) has not noticeably decreased¹. Nevertheless, policymakers need more accurate uncertainty estimates to make better decisions².

Figure 1: Uncertainty of projected sea-level rise for 2075 in Olympia, Washington under high emission scenario RCP 8.5 (ref. 23).

Results are from 18 models included in the CMIP5 experiment²⁴. The median climate model projection with no uncertainty is indicated by the vertical grey line, and uncertainty due to different climate model projections is shown by the histogram (white bars). The coloured curves represent cumulative uncertainty taking into account errors from the prediction of global mean sea-level rise from global mean temperature (red line), from the relation between Seattle sea-level rise and global mean sea-level rise (blue line) and from that between Seattle and Olympia sea-level rise (green line). Figure courtesy of Peter Guttorp, University of Washington.

• Full size image (84 KB)

Detailed guidance provided to authors of the IPCC AR5 and the US National Climate Assessment Report emphasizes the use of consistent terminology for describing uncertainty for risk communication. This includes a formal definition of terms such as 'likely' or 'unlikely' but, oddly, little advice is given about what statistical techniques should be adopted for uncertainty analysis^3, 4. At the least, more effort could be made to encourage authors to make use of modern techniques.

Historically, several compelling examples exist in which the development and application of innovative statistical methods resulted in breakthroughs in the understanding of the climate system (for example, Sir Gilbert Walker's research in the early twentieth century related to the El Niño–Southern Oscillation phenomenon⁵). We anticipate that similar success stories can be achieved for quantification of uncertainty in climate change.

Although climate observations and climate model output have different sources of error, both exhibit substantial spatial and temporal dependence. Hierarchical statistical models can capture these features in a more realistic manner⁶. These models adopt a 'divide and conquer' approach, breaking the problem into several layers of conceptually and computationally simpler conditional statistical models. The combination of these components produces an unconditional statistical model, whose structure can be quite complex and realistic. By using these models, uncertainty in observed climate trends and in projections of climate change can be substantially decreased. This decrease is obtained through 'borrowing strength', which exploits the fact that trends or projections ought to be similar at adjacent locations or grid points. Methods that are currently applied usually involve analysing the observations for each location — or the model output for each grid point — separately. Hierarchical statistical models can also be applied to combine different sources of climate information (for example, ground and satellite measurements), explicitly taking into account that they are recorded on different spatial and temporal scales^7, 8.

Even without any increase in computational power, statistical principles of experimental design can reduce uncertainty in the climate change projections produced by climate models through more efficient use of these limited resources⁹. Rather than allocating them uniformly across all combinations of the alternatives being examined, the allocation can be made in a manner that maximizes the amount of information obtained. For example, in assessing the impact of different global and regional climate models on climate projections, we do not need to examine all combinations of these models.

The recent IPCC Special Report Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation¹⁰, on which the IPCC AR5 relies, does not take full advantage of the well-developed statistical theory of extreme values¹¹. Instead, many results are presented in terms of spatially and temporally aggregated indices and/or consider only the frequency, not the intensity of extremes. Such summaries are not particularly helpful to decision-makers. Unlike the more familiar statistical theory for averages, extreme value theory does not revolve around the bell-shaped curve of the normal distribution. Rather, approximate distributions for extremes can depart far from the normal, including tails that decay as a power law. For variables such as precipitation and stream flow that possess power-law tails, conventional statistical methods would underestimate the return levels (that is, high quantiles) used in engineering design and, even more so, their uncertainties. Recent extensions of statistical methods for extremes make provision for non-stationarities such as climate change. In particular, they now provide non-stationary probabilistic models for extreme weather events, such as floods, as called for by Milly and colleagues¹².

We mention just one concrete example for which reliance on extreme value methods could help to resolve an issue with important policy implications. It has recently been claimed that, along with an increase in the mean, the probability distribution of temperature is becoming more skewed towards higher values^13, 14. But techniques based on extreme value theory do not detect this apparent increase in skewness¹⁵. Any increase is evidently an artefact of an inappropriate method of calculation of skewness when the mean is changing¹⁶.

Besides uncertainty quantification, the communication of uncertainty to policymakers is an important concern. Several fields conduct research on this topic, in addition to statistics (including decision analysis and risk analysis). Statisticians have made innovative contributions to the graphic display of probabilities to make communication more effective¹⁷, methods that have not yet found much, if any, use in climate change assessments (see Spiegelhalter et al.¹⁷ for an example of how uncertainties about future climate change are now presented).

It should be acknowledged that statisticians alone cannot solve these challenging problems in uncertainty quantification. Rather, increased collaboration between statistical and climate scientists is needed. Examples of current activities whose primary purpose is to stimulate such collaborations include: CliMathNet¹⁸, the Geophysical Statistics Project at the National Center for Atmospheric Research¹⁹, International Meetings on Statistical Climatology²⁰, the Nordic Network on Statistical Approaches to Regional Climate Models for Adaptation²¹ and the Research Network for Statistical Methods for Atmospheric and Oceanic Sciences²².

Recommendations

• Recommendations•

 

• References•

 

• Author information

To bring uncertainty quantification into the twenty-first century, we offer a number of suggestions (Box 1). Elaborated on here, these range from how climate change research is conducted to the process by which climate change assessments are produced.

Box 1: Recommendations to improve uncertainty quantification. [see above]

Full box

Whenever feasible, qualitative uncertainty assessments should be replaced with quantitative ones. At a minimum, a standard error should be attached to any estimate, along with a description of how it was calculated. Ideally, an entire probability distribution should be provided — innovative graphical techniques can be used to communicate these uncertainties in a more effective manner. In addition, modern statistical methods for spatio-temporal data, such as hierarchical models, should be used to reduce uncertainties in trend estimates for climate observations and projections of climate change. By taking into account spatial and temporal dependence, these techniques provide a powerful tool for detection of observed and projected changes in climate. To increase the accuracy with which the climate is monitored, various sources of information need to be combined using hierarchical statistical models. Such techniques can take into account differences in the uncertainties of, for instance, in situ and remotely sensed measurements.

Statistical principles of experimental design should be applied to climate change experiments using numerical models of the climate system. Through making more efficient use of computational resources, uncertainties in climate change projections can be reduced. Methods based on the statistical theory of extreme values should be used to quantify changes in the likelihood of extreme weather events, whether based on climate observations or on projections from climate models. In this way, information more useful to decision-makers about the risk of extreme events (for example, in terms of changing return levels) can be provided. Finally, to improve the quality of the treatment of uncertainty, at least one author with expertise in uncertainty analysis should be included on all chapters of IPCC and US national assessments. These authors could come from the field of statistics, as well as from other related fields including decision analysis and risk analysis.

If these recommendations are adopted, the improvements in uncertainty quantification would thereby help policymakers to better understand the risks of climate change and adopt policies that prepare the world for the future.

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New paper finds climate model assumptions on cloud-aerosol interactions may be off by ± 100%

A paper published today in Atmospheric Chemistry and Physics demonstrates the huge uncertainties of computer modeling of aerosol–cloud interaction effects, which are one of the "major sources of uncertainty in climate models." According to the authors, the standard deviation around the mean cloud condensation nuclei varies globally between a minimum of about ± 30% over some marine regions to ± 40–100% over most land areas and high latitudes. This is only one of the factors affecting clouds in climate models, and clouds are but one of the many major uncertainties in climate models. 

Dr. Judith Curry points out why climate models might be wrong in her "uncertainty monster" paper, and has pointed out for years the need for realistic assessments of the uncertainty of climate projections. This paper takes one step in that much need direction and shows only a tiny fraction [but still huge] of the "uncertainty monster." Meanwhile, the IPCC remains blissfully ignorant of the "uncertainty monster" and increases its confidence level from 90 to 95% without any basis in statistical analysis or science. 

Atmos. Chem. Phys., 13, 8879-8914, 2013 www.atmos-chem-phys.net/13/8879/2013/ doi:10.5194/acp-13-8879-2013

The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei

L. A. Lee¹, K. J. Pringle¹, C. L. Reddington¹, G. W. Mann¹, P. Stier², D. V. Spracklen¹, J. R. Pierce³, and K. S. Carslaw<sup>1

1</sup>Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK
²Department of Physics, University of Oxford, Oxford, UK
³Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA

Abstract. Aerosol–cloud interaction effects are a major source of uncertainty in climate models so it is important to quantify the sources of uncertainty and thereby direct research efforts. However, the computational expense of global aerosol models has prevented a full statistical analysis of their outputs. Here we perform a variance-based analysis of a global 3-D aerosol microphysics model to quantify the magnitude and leading causes of parametric uncertainty in model-estimated present-day concentrations of cloud condensation nuclei (CCN). Twenty-eight model parameters covering essentially all important aerosol processes, emissions and representation of aerosol size distributions were defined based on expert elicitation. An uncertainty analysis was then performed based on a Monte Carlo-type sampling of an emulator built for each model grid cell. The standard deviation around the mean CCN varies globally between about ±30% over some marine regions to ±40–100% over most land areas and high latitudes, implying that aerosol processes and emissions are likely to be a significant source of uncertainty in model simulations of aerosol–cloud effects on climate. Among the most important contributors to CCN uncertainty are the sizes of emitted primary particles, including carbonaceous combustion particles from wildfires, biomass burning and fossil fuel use, as well as sulfate particles formed on sub-grid scales. Emissions of carbonaceous combustion particles affect CCN uncertainty more than sulfur emissions. Aerosol emission-related parameters dominate the uncertainty close to sources, while uncertainty in aerosol microphysical processes becomes increasingly important in remote regions, being dominated by deposition and aerosol sulfate formation during cloud-processing. The results lead to several recommendations for research that would result in improved modelling of cloud–active aerosol on a global scale.

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