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AGU journal highlights -- April 13, 2011

The following highlights summarize research papers that have been recently published in Geophysical Research Letters (GRL).

1. Little Ice Age resulted from more than just solar calm

Total solar irradiance (TSI), essentially a measure of the amount of light the Sun puts out, varies with the 11-year sunspot cycle and influences Earth's climate, especially when TSI is notably higher or lower than its average values. It had been thought that TSI was especially low during a period known as the Little Ice Age, which began in the late 17th century, coinciding with a period of unusually low sunspot activity known as the Maunder Minimum. However, Schrijver et al. now suggest that TSI during that period may not have been as low as previously thought. They analyze direct measurements of solar magnetic activity during the recent 2008 to 2009 period of low sunspot activity, which they argue was similar to the activity level during the Maunder Minimum.

They find that even when there were no sunspots, the Sun had a baseline level of magnetic activity. This baseline had not been taken into account in previous estimates of TSI during the Maunder Minimum, which were based solely on sunspot numbers. Therefore, the authors suggest that earlier estimates of the TSI during the Maunder Minimum were too low. The researchers argue that the Maunder Minimum probably had levels of magnetic activity and TSI similar to 2008 and 2009 values, and therefore factors other than low solar irradiance resulting from low sunspot activity must have contributed to the Little Ice Age.

Source: Geophysical Research Letters, doi:10.1029/2011GL046658, 2011

Title: The minimal solar activity in 2008 to 2009 and its implications for long-term climate modeling

Authors: C. J. Schrijver: Lockheed Martin Advanced Technology Center, Palo Alto, California, USA; W. C. Livingston: National Solar Observatory, Tucson, Arizona, USA; T. N. Woods: Laboratory for Atmospheric and Space Physics, University of Colorado at Boulder, Boulder, Colorado, USA; R. A. Mewaldt: Space Radiations Laboratory, California Institute of Technology, Pasadena, California, USA.

2. Glacial dust carries iron to the Gulf of Alaska

Iron is an essential nutrient for phytoplankton in the ocean. How does iron get to the ocean? In the Gulf of Alaska the sources of iron and the processes that transport iron to the ocean are not well quantified. Crusius et al. combine satellite, meteorological, and other data to infer one source of "bioavailable" iron: dust from the Gulf of Alaska coastline.

Glacial erosion causes large amounts of fine-grained sediment, known as glacial flour, to be deposited near the coast. Some of this material can be resuspended by the wind and transported hundreds of kilometers from shore during dust storm events that occur at least annually, and most often in the fall, when river levels are low and sediments are exposed. The authors find that glacial flour dust storms are a significant source of iron to the Gulf of Alaska. They estimate the amount of iron-containing dust transported to the Gulf of Alaska from the Copper River valley during one 2006 dust storm to be between 25 and 80 thousand metric tons (28 to 88 thousand tons (U.S.)). If glaciers continue their present-day pattern of increasing mass loss due to a warming climate, more glacial flour may be transported to the Gulf of Alaska by this and other mechanisms, affecting phytoplankton growth and Gulf of Alaska ecosystems.

Source: Geophysical Research Letters, doi:10.1029/2010GL046573, 2011

Title: Glacial flour dust storms in the Gulf of Alaska: Hydrologic and meteorological controls and their importance as a source of bioavailable iron

Authors: John Crusius and Andrew W. Schroth: Woods Hole Coastal and Marine Science Center, U.S. Geological Survey, Woods Hole, Massachusetts, USA; Santiago Gass: Goddard Earth Sciences and Technology Center, University of Maryland Baltimore County, Baltimore, Maryland, USA; Christopher M. Moy: Woods Hole Coastal and Marine Science Center, U.S. Geological Survey, Woods Hole, Massachusetts, USA; and Geology Department, University of Otago, Dunedin, New Zealand; Robert C. Levy: Science Systems and Applications, Inc., Lanham, Maryland, USA; Myrna Gatica: School of Earth and Environmental Sciences, Queens College, City University of New York, Flushing, New York, USA.

3. Model suggests how to end Haitian cholera epidemic

Since early November 2010, a deadly cholera epidemic has been spreading across the Caribbean nation of Haiti, killing thousands of people and infecting hundreds of thousands. While infection rates are being actively monitored, health organizations have been left without a clear understanding of exactly how the disease has spread across Haiti. Cholera can spread through exposure to contaminated water, and the disease travels over long distances if an infected individual moves around the country. Using representations of these two predominant dispersion mechanisms, along with information on the size of the susceptible population, the number of infected individuals, and the aquatic concentration of the cholera-causing bacterium for more than 500 communities, Bertuzzo et al. designed a model that was able to accurately reproduce the progression of the Haitian cholera epidemic.

The authors' prediction for further spreading of the diseasemade in late December 2010 and supported by more recent datawas that the infection rate would begin to taper off in early January. They forecast that the bulk of the cases would occur near the seat of the epidemic, in the Artibonite department, as well as in and around the Haitian capital, Port-au-Prince, in the Ouest department.

Since the researchers' model is able to differentiate between the two major avenues of cholera transmissionby surface water and by infected individualsthey are able to assess how interventions that target these two mechanisms might be used to stem the progression of the disease. The authors argue that at this stage of the epidemic, emphasis should be placed on ensuring clean food and drinking water rather than pursuing less effective vaccination campaigns. The authors suggest that reducing in 1 month the exposure to contaminated food and water by 40 percent could severely limit the perpetuation of the epidemic.

See related blog post:

Source: Geophysical Research Letters, doi:10.1029/2011GL046823, 2011

Title: Prediction of the spatial evolution and effects of control measures for the unfolding Haiti cholera outbreak

Authors: E. Bertuzzo, L. Mari, L. Righetto, and A. Rinaldo: Laboratory of Ecohydrology, ECHO, ISTE, ENAC, cole Polytechnique Fdrale de Lausanne, Lausanne, Switzerland; M. Gatto and R. Casagrandi: Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milan, Italy; M. Blokesch: Laboratory of Molecular Microbiology, UPBLO, GHI, SV, cole Polytechnique Fdrale de Lausanne, Lausanne, Switzerland; I. Rodriguez-Iturbe: Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA.

4. Deadly 2010 Russian heat wave: Not from climate change

Although some people may try to ascribe specific extreme weather events to climate change, global warming cannot be held responsible for recent weather events such as the 2010 Russian heat wave. Using climate simulations and a comparison against historical conditions, Dole et al. assess the influence of greenhouse gases, aerosols, anomalous sea surface temperatures, and other potential climate forcings on the likelihood and magnitude of the 2010 Russian heat wave. The authors suggest that the heat wave, which lasted from late June to mid-August and was responsible for thousands of deaths, widespread wild fires, and devastating crop loss, fell well within the bounds of natural climate variability.

The authors find that none of the tested climate factors showed appreciable ability to predict the extreme temperatures seen throughout the heat wave. Additionally, the researchers' historical analysis reveals that July temperatures, as well as the temperature variability, for the affected region of western Russia showed no significant trend over the past 130 years. They note that the top 10 hottest July days for the region were distributed randomly across the historical period, although global averages do show clustering in the past 2 decades.

While the researchers argue that there is no reason to have anticipated the extreme nature of the heat wave from a historical perspective, Matsueda (Geophys. Res. Lett., 38, L06801, doi:10.1029/2010GL046557, 2011. See Highlight 5, below.) suggests that many of the details of this particular event could have been predicted using short-term weather forecasting.

See related press release:

Source: Geophysical Research Letters, doi:10.1029/2010GL046582, 2011

Title: Was there a basis for anticipating the 2010 Russian heat wave?

Authors: Randall Dole and Martin Hoerling; Physical Sciences Division, Earth System Research Laboratory, NOAA, Boulder, Colorado, USA; Judith Perlwitz, Jon Eischeid, Philip Pegion, Tao Zhang, Xiao-Wei Quan, Taiyi Xu, and Donald Murray: Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, Boulder, Colorado, USA.

5. Cause of 2010 Russian heat wave was largely predictable

From June to August 2010, a stable high-pressure air mass hung in the sky over western Russia. This episode of "atmospheric blocking" drove up temperatures, causing thousands of deaths and breaking temperature records across the country. Strong heat waves like the one in Russia increase their potential to do damage the longer they persist, putting added stress on populations susceptible to dehydration, reduced air quality, and other heat-related illnesses. Long durations of consistently high temperatures also increase the likelihood of drought-driven crop loss and wildfires, both of which devastated the Russian countryside and economy. In a retrospective analysis, Matsueda finds that the forecasting systems in place to monitor the atmospheric blocking over western Russia faltered, failing to predict the extended duration of the blocking as the record-breaking temperatures crept into early August.

Comparing the predictions of five medium-range ensemble forecasting models and the European Centre for Medium-Range Weather Forecasts' highest-resolution deterministic forecasting model, the author finds that the onset of the atmospheric blocking and resultant temperature increases, from early June to late July, was easily predicted by the models up to 9 days in advance. However, the extended blocking, stretching from 30 July to 9 August, and the peak temperature anomaly of 11.3C (30.3F), were not predicted, with the models underestimating both the magnitude and duration of the extreme event.

The heat wave, while largely predictable on short, weather-driven timescales, appears not to be the product of long-term climate changes. Instead, as discussed by Dole et al. (Geophys. Res. Lett., 38, L06702, doi:10.1029/2010GL046582, 2011. See Highlight 4, above), the heat wave falls within the realm of natural variability.

See related press release:

Source: Geophysical Research Letters, doi:10.1029/2010GL046557, 2011

Title: Predictability of Euro-Russian blocking in summer of 2010

Authors: Mio Matsueda: Japan Agency for Marine-Earth Science and Technology, Tsukuba, Japan.


Contact: Peter Weiss
American Geophysical Union

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