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Sea-level rise: Where we stand at the start of 2013 — Part 2

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This is Part 2 of my thoughts on the state of sea-level research. Here is Part 1.

Sea-level cycles?

A topic that keeps coming up in the literature is the discussion on a (roughly) 60-year cycle in sea level data; a nice recent paper on this is Chambers et al. in GRL (2012). One thing I like about this paper is its careful discussion of the sampling issue of the tide gauges, which means that variability in the tide gauges is not necessarily variability in the true global mean sea level (see Part 1 of this post). I want to add some thoughts on the interpretation of this variability. Consider this graph from my Response to Comments in Science (2007):


Fig. 1: Fifteen-year averages of the global mean temperature (blue, °C, GISS data) and rate of sea level rise (red, cm/year, Church&white data), both detrended.
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References

  1. D.P. Chambers, M.A. Merrifield, and R.S. Nerem, "Is there a 60‐year oscillation in global mean sea level?", Geophysical Research Letters, vol. 39, 2012. http://dx.doi.org/10.1029/2012GL052885
  2. S. Rahmstorf, "Response to Comments on "A Semi-Empirical Approach to Projecting Future Sea-Level Rise"", Science, vol. 317, pp. 1866-1866, 2007. http://dx.doi.org/10.1126/science.1141283

Sea-level rise: Where we stand at the start of 2013

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Progress has been made in recent years in understanding the observed past sea-level rise. As a result, process-based projections of future sea-level rise have become dramatically higher and are now closer to semi-empirical projections. However, process-based models still underestimate past sea-level rise, and they still project a smaller rise than semi-empirical models.

Sea-level projections were probably the most controversial aspect of the 4th IPCC report, published in 2007. As an author of the paleoclimate chapter, I was involved in some of the sea-level discussions during preparation of the report, but I was not part of the writing team for the projections. At the core of the controversy were the IPCC-projections which are based on process models (i.e. models that aim to simulate individual processes like thermal expansion or glacier melt). Many scientists felt that these models were not mature and understated the sea-level rise to be expected in future, and the IPCC report itself documented the fact that the models seriously underestimated past sea-level rise. (See our in-depth discussion published after the 4th IPCC report appeared.) That was confirmed again with the most recent data in Rahmstorf et al. 2012.
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References

  1. S. Rahmstorf, G. Foster, and A. Cazenave, "Comparing climate projections to observations up to 2011", Environmental Research Letters, vol. 7, pp. 044035, 2012. http://dx.doi.org/10.1088/1748-9326/7/4/044035

On Sensitivity Part II: Constraining Cloud Feedback without Cloud Observations

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Guest Commentary by Karen M. Shell, Oregon State University

Link to Part I.

Clouds are very pesky for climate scientists. Due to their high spatial and temporal variability, as well as the many processes involved in cloud droplet formation, clouds are difficult to model. Furthermore, clouds have competing effects on solar and terrestrial radiation. Increases in clouds increase reflected sunlight (a cooling effect) but also increase the greenhouse effect (a warming effect). The net effect of clouds at a given location depends the kind of clouds (stratus, cumulus etc.), their distribution in the vertical and on which radiative effect dominates.

Not only is it difficult to correctly represent clouds in climate models, but estimating how clouds and their radiative effects will change with global warming (i.e., the cloud feedback) is very difficult. Other physical feedbacks have more obvious links between temperature and the climate variable. For example, we expect and have strong evidence for the increase in water vapor in a warmer climate due to the increased saturation specific humidity, or the reduced reflection of sunlight due to the melting of snow and ice at higher temperature. However, there isn’t a simple thermodynamic relationship between temperature and cloud amount, and the complexities in the radiative impacts of clouds mean that an increase in clouds in one location may result in net heating, but would correspond to a cooling elsewhere. Thus, most of the uncertainty in the response of climate models to increases in CO2 is due to the uncertainty of the cloud feedback.

So how cool is it then that the recent paper by Fasullo and Trenberth estimates the net climate sensitivity without getting into the details of the cloud feedback then? Quite cool.
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References

  1. J.T. Fasullo, and K.E. Trenberth, "A Less Cloudy Future: The Role of Subtropical Subsidence in Climate Sensitivity", Science, vol. 338, pp. 792-794, 2012. http://dx.doi.org/10.1126/science.1227465

On sensitivity: Part I

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Climate sensitivity is a perennial topic here, so the multiple new papers and discussions around the issue, each with different perspectives, are worth discussing. Since this can be a complicated topic, I’ll focus in this post on the credible work being published. There’ll be a second part from Karen Shell, and in a follow-on post I’ll comment on some of the recent games being played in and around the Wall Street Journal op-ed pages.

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What is signal and what is noise?

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The recent warming has been more pronounced in the Arctic Eurasia than in many other regions on our planet, but Franzke (2012) argues that only one out of 109 temperature records from this region exhibits a significant warming trend. I think that his conclusions were based on misguided analyses.

The analysis did not sufficiently distinguish between signal and noise, and mistaking noise for signal will give misguided conclusions.

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References

  1. C. Franzke, "On the statistical significance of surface air temperature trends in the Eurasian Arctic region", Geophysical Research Letters, vol. 39, 2012. http://dx.doi.org/10.1029/2012GL054244

A review of cosmic rays and climate: a cluttered story of little success

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A number of blogs were excited after having leaked the second-order draft of IPCC document, which they interpreted as a “game-changing admission of enhanced solar forcing”.

However, little evidence remains for a link between galactic cosmic rays (GCR) and variations in Earth’s cloudiness. Laken et al. (2012) recently provided an extensive review of the study of the GCR and Earth’s climate, from the initial work by Ney (1959) to the latest findings from 2012.

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References

  1. B.A. Laken, E. Pallé, J. Čalogović, and E.M. Dunne, "A cosmic ray-climate link and cloud observations", Journal of Space Weather and Space Climate, vol. 2, pp. A18, 2012. http://dx.doi.org/10.1051/swsc/2012018

The heat is on in West Antarctica

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Eric Steig

Regular followers of RealClimate will be aware of our publication in 2009 in Nature, showing that West Antarctica — the part of the Antarctic ice sheet that is currently contributing the most to sea level rise, and which has the potential to become unstable and contribute a lot more (3 meters!) to sea level rise in the future — has been warming up for the last 50 years or so.

Our paper was met with a lot of skepticism, and not just from the usual suspects. A lot of our fellow scientists, it seems, had trouble getting over their long-held view (based only on absence of evidence) that the only place in Antarctica that was warming up was the Antarctica Peninsula. To be fair, our analysis was based on interpolation, using statistics to fill in data where it was absent, so we really hadn’t proven anything; we’d only done an analysis that pointed (strongly!) in a particular direction.

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Online video lectures on climate change

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For those who’d like to get the basics of climate change explained first-hand by a climate scientist, here are two video lectures.

In the first, I show some of the basic data sets and findings about global warming, including some comments on historic land marks of our science.

The second lecture deals with the impacts of climate change (with a focus on extreme events and sea-level rise) and the possibilities for holding global warming below 2°C.

These lectures form part of a broader lecture course called World in Transition. It includes 11 themes, presented by the members of the German Advisory Council on Global Change (WBGU). This is a body of experts appointed by the German government and advising it on global change issues. The lecture series, for which international students can enrol and earn credit points, is based on the WBGU flagship report World in Transition – A Social Contract for Sustainability. This report describes how the transition to a sustainable, climate-friendly global economy and life style can be achieved.

Improving the Tropical Cyclone Climate Record

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Guest Commentary by Christopher Hennon (UNC Asheville)

Get involved in a new citizen science project at CycloneCenter.org.

The poor quality of the tropical cyclone (TC) data record provides severe constraints on the ability of climate scientists to: a) determine to what degree TCs have responded to shifts in climate, b) evaluate theories on how TCs will respond to climate change in the future. The root cause for the poor data is the severity of the TC conditions (e.g. high wind, rough seas) and the remoteness of these storms – the vast majority of which form and remain well away from most observing networks. Thus, most TCs are not observed directly and those that are (with buoys, aircraft reconnaissance, ships) are often not sampled sufficiently (see the IBTrACS, (Knapp et al., 2010)).

This leaves tropical cyclone forecasters, who are ultimately responsible for recording TC tracks and intensities (i.e. maximum wind speeds), with a challenging problem. Fortunately, there is a tool called the Dvorak Technique which allows forecasters to make a reasonable determination of the TC intensity by simply analyzing a single infrared or visible satellite image, which is almost always available Velden et al., 2006). The technique calls for the analyst to determine the center location of the system, the cloud pattern type, the degree of organization of the pattern, and the intensity trend. A maximum surface wind speed is determined after the application of a number of rules and constraints.

Hurricane Gay (1992)The Dvorak Technique has been used for many years at all global tropical cyclone forecast centers and has been shown in many cases to yield a good estimate of maximum TC wind speed, when applied properly (Knaff et al., 2010). However, there is a level of analyst subjectivity inherent in the procedure; the cloud patterns are not always clear, it is sometimes difficult to accurately determine the storm center and the rules and constraints have been interpreted and applied differently across agencies. This introduces heterogeneity in the global TC record since the Dvorak Technique is usually the only available tool for assessing the maximum wind speed.

There has been recent work to eliminate the human element in the Dvorak Technique by automating the procedure. The Advanced Dvorak Technique (ADT) uses objective storm center and cloud pattern schemes to remove the subjectivity (Olander and Velden, 2007). All other classification rules and constraints are then applied and combined with additional statistical information to produce automated intensity estimates. Although the ADT skill is comparable to experienced human Dvorak analysts, large errors can occur if the scene type is not identified properly.

A new crowd sourcing project, called Cyclone Center, embraces the human element by enabling the public to perform a simplified version of the Dvorak Technique to analyze historical global tropical cyclone (TC) intensities (Hennon, 2012). Cyclone Center’s primary goal is to resolve discrepancies in the recent global TC record arising principally from inconsistent development of tropical cyclone intensity data. The Cyclone Center technique standardizes the classification procedure by condensing the Dvorak Technique to a few simple questions that can be answered by global, nonprofessional users.

One of the main advantages of this approach is the inclusion of thousands of users, instead of the 1-3 who would normally classify a TC image. This allows the computation of measures of uncertainty in addition to a mean intensity. Nearly 300,000 images, encompassing all global TCs that formed from 1978-2009, will be classified 30 times each – a feat that would take a dedicated team of twenty Dvorak-trained experts about 12 years to complete. Citizen scientists have already performed over 100,000 classifications since the project launch in September. Once the project is complete, a new dataset of global TC tracks and intensities will be made available to the community to contribute to our efforts to provide the best possible TC data record.

Interested readers are encouraged to learn more about and participate in the project at the cyclonecenter.org website (there are some FAQ on the project blog). The CycloneCenter project is a collaboration between the Citizen Science Alliance, NOAA National Climatic Data Center (NCDC), University of North Carolina at Asheville, and the Cooperative Institute for Climate and Satellites (CICS) – North Carolina.

References

  1. K.R. Knapp, M.C. Kruk, D.H. Levinson, H.J. Diamond, and C.J. Neumann, "The International Best Track Archive for Climate Stewardship (IBTrACS)", Bulletin of the American Meteorological Society, vol. 91, pp. 363-376, 2010. http://dx.doi.org/10.1175/2009BAMS2755.1
  2. C. Velden, B. Harper, F. Wells, J.L. Beven, R. Zehr, T. Olander, M. Mayfield, C.. Guard, M. Lander, R. Edson, L. Avila, A. Burton, M. Turk, A. Kikuchi, A. Christian, P. Caroff, and P. McCrone, "The Dvorak Tropical Cyclone Intensity Estimation Technique: A Satellite-Based Method that Has Endured for over 30 Years", Bulletin of the American Meteorological Society, vol. 87, pp. 1195-1210, 2006. http://dx.doi.org/10.1175/BAMS-87-9-1195
  3. J.A. Knaff, D.P. Brown, J. Courtney, G.M. Gallina, and J.L. Beven, "An Evaluation of Dvorak Technique–Based Tropical Cyclone Intensity Estimates", Weather and Forecasting, vol. 25, pp. 1362-1379, 2010. http://dx.doi.org/10.1175/2010WAF2222375.1
  4. T.L. Olander, and C.S. Velden, "The Advanced Dvorak Technique: Continued Development of an Objective Scheme to Estimate Tropical Cyclone Intensity Using Geostationary Infrared Satellite Imagery", Weather and Forecasting, vol. 22, pp. 287-298, 2007. http://dx.doi.org/10.1175/WAF975.1
  5. C.C. Hennon, "Citizen scientists analyzing tropical cyclone intensities", Eos, Transactions American Geophysical Union, vol. 93, pp. 385-387, 2012. http://dx.doi.org/10.1029/2012EO400002