A Pan-Arctic Airborne Sea Ice Observation System

We present an Arctic sea-ice observation system that focuses on unique direct observations of sea ice plus snow thickness. A network of research institutions, the Alfred Wegener Institute, York University and the Norwegian Polar Institute, maintain an observation system that is embedded in several national and international projects and supported by research partners. Activities in the field include the use of long-range polar research aircraft and helicopter operations from research icebreakers and bases on land. Data collections are based on electromagnetic induction sounding and consistent time series are available in key regions of the Arctic Ocean since 2001. The increased use of polar research aircrafts in recent years has resulted in several initiatives that aim for long-term observations of ice thickness during seasonal minimum and maximum sea-ice extent in the Arctic. The scientific payload of the research aircraft of type Basler BT-67 and its capability to fly low-altitude surveys makes it an ideal tool for the validation and on-going verification of various satellite remote sensing products. The availability of airborne sea-ice thickness information spans the periods of different satellite sea-ice thickness retrieval concepts, such as the radar altimeters from Envisat and CryoSat-2 as well as the laser altimeter from ICESat-1 and -2. Wherever possible, the airborne surveys are accompanied by in-situ observations on the ice surface to compile a hierarchy of validation data from local to basin scales. Results of the observation network have found broad use for studying inter-annual variability and changes of sea ice thickness as well as the validation of satellite data products. We identify a gap of observations over the multi-year sea ice zone during the melt season and early freeze-up. We also stress the need for the continuation of a coordinated observational program that has produced a time series of sea ice thickness only paralleled by submarine observations. We plan to augment the observation system by simultaneous measurements of snow depth and to investigate opportunities for technological advances, such as the utilization of unmanned aerial systems

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

The Drosophila speciation factor HMR localizes to genomic insulator sites

Hybrid incompatibility between Drosophila melanogaster and D. simulans is caused by a lethal interaction of the proteins encoded by the Hmr and Lhr genes. In D. melanogaster the loss of HMR results in mitotic defects, an increase in transcription of transposable elements and a deregulation of heterochromatic genes. To better understand the molecular mechanisms that mediate HMR's function, we measured genome-wide localization of HMR in D. melanogaster tissue culture cells by chromatin immunoprecipitation. Interestingly, we find HMR localizing to genomic insulator sites that can be classified into two groups. One group belongs to gypsy insulators and another one borders HP1a bound regions at active genes. The transcription of the latter group genes is strongly affected in larvae and ovaries of Hmr mutant flies. Our data suggest a novel link between HMR and insulator proteins, a finding that implicates a potential role for genome organization in the formation of species

State of the climate in 2018

In 2018, the dominant greenhouse gases released into Earth’s atmosphere—carbon dioxide, methane, and nitrous oxide—continued their increase. The annual global average carbon dioxide concentration at Earth’s surface was 407.4 ± 0.1 ppm, the highest in the modern instrumental record and in ice core records dating back 800 000 years. Combined, greenhouse gases and several halogenated gases contribute just over 3 W m−2 to radiative forcing and represent a nearly 43% increase since 1990. Carbon dioxide is responsible for about 65% of this radiative forcing. With a weak La Niña in early 2018 transitioning to a weak El Niño by the year’s end, the global surface (land and ocean) temperature was the fourth highest on record, with only 2015 through 2017 being warmer. Several European countries reported record high annual temperatures. There were also more high, and fewer low, temperature extremes than in nearly all of the 68-year extremes record. Madagascar recorded a record daily temperature of 40.5°C in Morondava in March, while South Korea set its record high of 41.0°C in August in Hongcheon. Nawabshah, Pakistan, recorded its highest temperature of 50.2°C, which may be a new daily world record for April. Globally, the annual lower troposphere temperature was third to seventh highest, depending on the dataset analyzed. The lower stratospheric temperature was approximately fifth lowest. The 2018 Arctic land surface temperature was 1.2°C above the 1981–2010 average, tying for third highest in the 118-year record, following 2016 and 2017. June’s Arctic snow cover extent was almost half of what it was 35 years ago. Across Greenland, however, regional summer temperatures were generally below or near average. Additionally, a satellite survey of 47 glaciers in Greenland indicated a net increase in area for the first time since records began in 1999. Increasing permafrost temperatures were reported at most observation sites in the Arctic, with the overall increase of 0.1°–0.2°C between 2017 and 2018 being comparable to the highest rate of warming ever observed in the region. On 17 March, Arctic sea ice extent marked the second smallest annual maximum in the 38-year record, larger than only 2017. The minimum extent in 2018 was reached on 19 September and again on 23 September, tying 2008 and 2010 for the sixth lowest extent on record. The 23 September date tied 1997 as the latest sea ice minimum date on record. First-year ice now dominates the ice cover, comprising 77% of the March 2018 ice pack compared to 55% during the 1980s. Because thinner, younger ice is more vulnerable to melting out in summer, this shift in sea ice age has contributed to the decreasing trend in minimum ice extent. Regionally, Bering Sea ice extent was at record lows for almost the entire 2017/18 ice season. For the Antarctic continent as a whole, 2018 was warmer than average. On the highest points of the Antarctic Plateau, the automatic weather station Relay (74°S) broke or tied six monthly temperature records throughout the year, with August breaking its record by nearly 8°C. However, cool conditions in the western Bellingshausen Sea and Amundsen Sea sector contributed to a low melt season overall for 2017/18. High SSTs contributed to low summer sea ice extent in the Ross and Weddell Seas in 2018, underpinning the second lowest Antarctic summer minimum sea ice extent on record. Despite conducive conditions for its formation, the ozone hole at its maximum extent in September was near the 2000–18 mean, likely due to an ongoing slow decline in stratospheric chlorine monoxide concentration. Across the oceans, globally averaged SST decreased slightly since the record El Niño year of 2016 but was still far above the climatological mean. On average, SST is increasing at a rate of 0.10° ± 0.01°C decade−1 since 1950. The warming appeared largest in the tropical Indian Ocean and smallest in the North Pacific. The deeper ocean continues to warm year after year. For the seventh consecutive year, global annual mean sea level became the highest in the 26-year record, rising to 81 mm above the 1993 average. As anticipated in a warming climate, the hydrological cycle over the ocean is accelerating: dry regions are becoming drier and wet regions rainier. Closer to the equator, 95 named tropical storms were observed during 2018, well above the 1981–2010 average of 82. Eleven tropical cyclones reached Saffir–Simpson scale Category 5 intensity. North Atlantic Major Hurricane Michael’s landfall intensity of 140 kt was the fourth strongest for any continental U.S. hurricane landfall in the 168-year record. Michael caused more than 30 fatalities and $25 billion (U.S. dollars) in damages. In the western North Pacific, Super Typhoon Mangkhut led to 160 fatalities and$6 billion (U.S. dollars) in damages across the Philippines, Hong Kong, Macau, mainland China, Guam, and the Northern Mariana Islands. Tropical Storm Son-Tinh was responsible for 170 fatalities in Vietnam and Laos. Nearly all the islands of Micronesia experienced at least moderate impacts from various tropical cyclones. Across land, many areas around the globe received copious precipitation, notable at different time scales. Rodrigues and Réunion Island near southern Africa each reported their third wettest year on record. In Hawaii, 1262 mm precipitation at Waipā Gardens (Kauai) on 14–15 April set a new U.S. record for 24-h precipitation. In Brazil, the city of Belo Horizonte received nearly 75 mm of rain in just 20 minutes, nearly half its monthly average. Globally, fire activity during 2018 was the lowest since the start of the record in 1997, with a combined burned area of about 500 million hectares. This reinforced the long-term downward trend in fire emissions driven by changes in land use in frequently burning savannas. However, wildfires burned 3.5 million hectares across the United States, well above the 2000–10 average of 2.7 million hectares. Combined, U.S. wildfire damages for the 2017 and 2018 wildfire seasons exceeded $40 billion (U.S. dollars)

HMR localisation to repetitive elements.

<p><b>(A)</b> HMR ChIP tag enrichment at repetitive DNA elements. To identify enriched sequences the enrichment (log2-fold) over input is plotted against the RPKM of an individual repeat sequence from RepBase. Repeats with less than 2-fold enrichment are not displayed. <b>(B)</b> ChIP tag density of HMR and the <i>gypsy</i>-insulator proteins CP190, Mod(mdg4), Su(Hw) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171798#pone.0171798.ref039" target="_blank">39</a>] across the repetitive <i>gypsy</i> retrotransposon sequence. The <i>gypsy</i> insulator sequence at the 5' end is highlighted in <i>green</i>.</p

HMR localizes to genomic insulators and the <i>gypsy</i> transposon.

<p><b>(A)</b> Sequence motifs identified within HMR peak regions. The corresponding motif logo, p-value of enrichment, percentage of regions with this motif and putative binding factors are indicated. Dashed arrows mark the sequence that matches the published binding sites of Su(Hw) and BEAF-32 (see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171798#pone.0171798.s002" target="_blank">S2A Fig</a>) <b>(B)</b> Peak overlap of HMR with peaks of the insulator proteins CP190, Mod(mdg4), Su(Hw), CTCF [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171798#pone.0171798.ref039" target="_blank">39</a>] and BEAF-32 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171798#pone.0171798.ref040" target="_blank">40</a>]. The number of HMR peaks is indicated depending on their colocalization with known boundary factors. Groups with less than 11 members are not displayed. Su(Hw)-containing <i>gypsy</i>-like groups are depicted in <i>green</i>, non <i>gypsy</i>-like groups in <i>orange</i>. Combinations that contain less than ten HMR peaks are not shown. <b>(C)</b> Genome browser view of the Su(Hw) binding region 1A-2. ChIP signals of HMR and known <i>gypsy</i> binding factors are shown. The 1A-2 insulator is highlighted in <i>green</i>.</p

HMR genomic localization to <i>gypsy</i>-like insulator sites is dependent on CP190.

<p><b>(A)</b> Western Blot of cell lysates after treatment with specific and control dsRNA shows an efficient knock-down of HMR and CP190. <b>(B)</b> Venn diagram of the overlap between HMR, CP190, Mod(mdg4) and Su(Hw) peaks [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171798#pone.0171798.ref039" target="_blank">39</a>] classifying HMR peaks as <i>gypsy</i>-like (highlighted in <i>green</i>) and non <i>gypsy</i>-like (highlighted in <i>orange</i>). <b>(C)</b> Composite analysis of HMR ChIP signals and Histone H3 ChIP signals at genomic HMR peak positions according to the groups defined in <b>(B)</b>. <b>(D)</b> Quantification of the fold-change of HMR ChIP enrichment upon CP190 RNAi and HMR RNAi. Box plots represent the fold-change of normalized HMR ChIP tag number aligned to 200 bp wide HMR peak regions. Peak regions with less than 50 aligned tags were excluded from the analysis. Significance of difference was estimated with p-values calculated with Wilcoxon rank sum test [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171798#pone.0171798.ref072" target="_blank">72</a>].</p

HMR borders HP1a together with BEAF-32 at the TSS of actively transcribed genes and enhances their transcription.

<p><b>(A)</b> Heatmaps of HMR, HP1a, BEAF-32 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171798#pone.0171798.ref040" target="_blank">40</a>], Su(Hw), Mod(mdg4), CP190 and CTCF [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171798#pone.0171798.ref039" target="_blank">39</a>] ChIP signals. All signals are centered around the HMR binding sites, clustered according to adjacent HP1a signals and sorted by HMR intensity. <b>(B)</b> Sequence motifs identified within HMR peak regions from class 1 and class 2 based on HOMER motif analysis. The corresponding motif logo, p-value of enrichment, percentage of regions with this motif, and putative binding factors are indicated. Dashed arrows mark the sequence that matches the published binding sites of Su(Hw) and BEAF-32 (see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171798#pone.0171798.s002" target="_blank">S2A Fig</a>). <b>(C)</b> Distribution of class 1 and class 2 HMR peaks among various genomic landmarks. <b>(D)</b> Box plot showing the normalized RNA expression of all genes and HMR-bound genes (promoter/TSS annotated) in class 1 and in class 2. S2 cells RNA expression levels were used according to [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171798#pone.0171798.ref073" target="_blank">73</a>]. Significance of difference was estimated with p-values calculated with Wilcoxon signed rank test [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171798#pone.0171798.ref072" target="_blank">72</a>]. <b>(E)</b> Box plot showing the log2 fold change of protein coding gene transcripts of all analyzed genes and HMR-bound genes (promoter/TSS annotated) in class 1 and in class 2 comparing <i>Hmr</i> mutant against wild type flies. The RNA-Seq data comes from experiments done in <i>D</i>. <i>melanogaster</i> ovaries [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171798#pone.0171798.ref004" target="_blank">4</a>]. Significance of difference was estimated with p-values calculated with Wilcoxon rank sum test [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171798#pone.0171798.ref072" target="_blank">72</a>]. For both box plots the box represents the interval that contains the central 50% of the data with the line indicating the median. The length of the whiskers is 1.5 times the interquartile distance (IQD). <b>(F)</b> Histogram showing HMR peak density across the annotated <i>D</i>. <i>melanogaster</i> genome. Class 1 HMR binding sites are enriched at region 31, centromere-proximal regions and the 4th chromosome.</p

SMOS sea ice product: Operational application and validation in the Barents Sea marginal ice zone

Brightness temperatures at 1.4 GHz (L-band) measured by the Soil Moisture and Ocean Salinity (SMOS) Mission have been used to derive the thickness of sea ice. The retrieval method is applicable only for relatively thin ice and not during the melting period. Hitherto, the availability of ground truth sea ice thickness measurements for validation of SMOS sea ice products was mainly limited to relatively thick ice. The situation has improved with an extensive field campaign in the Barents Sea during an anomalous ice edge retreat and subsequent freeze-up event in March 2014. A sea ice forecast system for ship route optimisation has been developed and was tested during this field campaign with the ice-strengthened research vessel RV Lance. The ship cruise was complemented with coordinated measurements from a helicopter and the research aircraft Polar 5. Sea ice thickness was measured using an electromagnetic induction (EM) system from the bow of RV Lance and another EM-system towed below the helicopter. Polar 5 was equipped among others with the L-band radiometer EMIRAD-2. The experiment yielded a comprehensive data set allowing the evaluation of the operational forecast and route optimisation system as well as the SMOS-derived sea ice thickness product that has been used for the initialization of the forecasts. Two different SMOS sea ice thickness products reproduce the main spatial patterns of the ground truth measurements while the main difference being an underestimation of thick deformed ice. Ice thicknesses derived from the surface elevation measured by an airborne laser scanner and from simultaneous EMIRAD-2 brightness temperatures correlate well up to 1.5 m which is more than the previously anticipated maximal SMOS retrieval thickness