Commercial Arctic shipping through the Northeast Passage:routes, resources, governance, technology, and infrastructure

The Russian and Norwegian Arctic are gaining notoriety as an alternative maritime route connecting the Atlantic and Pacific Oceans and as sources of natural resources. The renewed interest in the Northeast Passage or the Northern Sea Route is fueled by a recession of Arctic sea ice coupled with the discovery of new natural resources at a time when emerging and global markets are in growing demand for them. Driven by the expectation of potential future economic importance of the region, political interest and governance has been rapidly developing, mostly within the Arctic Council. However, this paper argues that optimism regarding the potential of Arctic routes as an alternative to the Suez Canal is overstated. The route involves many challenges: jurisdictional disputes create political uncertainties; shallow waters limit ship size; lack of modern deepwater ports and search and rescue (SAR) capabilities requires ships to have higher standards of autonomy and safety; harsh weather conditions and free-floating ice make navigation more difficult and schedules more variable; and more expensive ship construction and operation costs lessen the economic viability of the route. Technological advances and infrastructure investments may ameliorate navigational challenges, enabling increased shipping of natural resources from the Arctic to global markets.Albert Buixadé Farré, Scott R. Stephenson, Linling Chen, Michael Czub, Ying Dai, Denis Demchev, Yaroslav Efimov, Piotr Graczyk, Henrik Grythe, Kathrin Keil, Niku Kivekäs, Naresh Kumar, Nengye Liu, Igor Matelenok, Mari Myksvoll, Derek O'Leary, Julia Olsen, Sachin Pavithran.A.P., Edward Petersen, Andreas Raspotnik, Ivan Ryzhov, Jan Solski, Lingling Suo, Caroline Troein, Vilena Valeeva, Jaap van Rijckevorsel and Jonathan Wightin

Atmospheric forcing validation for modeling the central Arctic

Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 34 (2007): L20706, doi:10.1029/2007GL031378.We compare daily data from the National Center for Atmospheric Research and National Centers for Environmental Prediction “Reanalysis 1” project with observational data obtained from the North Pole drifting stations in order to validate the atmospheric forcing data used in coupled ice-ocean models. This analysis is conducted to assess the role of errors associated with model forcing before performing model verifications against observed ocean variables. Our analysis shows an excellent agreement between observed and reanalysis sea level pressures and a relatively good correlation between observed and reanalysis surface winds. The observed temperature is in good agreement with reanalysis data only in winter. Specific air humidity and cloudiness are not reproduced well by reanalysis and are not recommended for model forcing. An example sensitivity study demonstrates that the equilibrium ice thickness obtained using NP forcing is two times thicker than using reanalysis forcing.This research is supported by the National Science Foundation Office of Polar Programs (under Cooperative Agreements Nos. OPP-0002239 and OPP-0327664) with the International Arctic Research Center, University of Alaska Fairbanks, NSF grant OPP- 0424864 and by Russian Foundation for Basic Research, No. 07-05-13576

Greenland surface air temperature changes from 1981 to 2019 and implications for ice-sheet melt and mass-balance change

We provide an updated analysis of instrumental Greenland monthly temperature data to 2019, focusing mainly on coastal stations but also analysing ice-sheet records from Swiss Camp and Summit. Significant summer (winter) coastal warming of ~1.7 (4.4) C occurred from 1991-2019, but since 2001 overall temperature trends are generally flat and insignificant due to a cooling pattern over the last 6-7 years. Inland and coastal stations show broadly similar temperature trends for summer. Greenland temperature changes are more strongly correlated with Greenland Blocking than with North Atlantic Oscillation changes. In quantifying the association between Greenland coastal temperatures and Greenland Ice Sheet (GrIS) mass-balance changes, we show a stronger link of temperatures with total mass balance rather than surface mass balance. Based on Greenland coastal temperatures and modelled mass balance for the 1972-2018 period, each 1C of summer warming corresponds to ~ (91) 116 Gt yr-1 of GrIS (surface) mass loss and a 26 Gt yr-1 increase in solid ice discharge. Given an estimated 4.0-6.6C of further Greenland summer warming according to the regional model MAR projections run under CMIP6 future climate projections (SSP5-8.5 scenario), and assuming that ice-dynamical losses and ice sheet topography stay similar to the recent past, linear extrapolation gives a corresponding GrIS global sea-level rise (SLR) contribution of ~10.0-12.6 cm by 2100, compared with the 8-27 cm (mean 15 cm) “likely” model projection range reported by IPCC (2019, SPM.B1.2). However, our estimate represents a lower limit for future GrIS change since fixed dynamical mass losses and amplified melt arising from both melt-albedo and melt-elevation positive feedbacks are not taken into account here

Sensitivity of L-band vegetation optical depth to carbon stocks in tropical forests: a comparison to higher frequencies and optical indices

Supplementary data to this article can be found online at https://doi.org/10.1016/j.rse.2019.111303.Monitoring vegetation carbon in tropical regions is essential to the global carbon assessment and to evaluate the actions oriented to the reduction of forest degradation. Mainly, satellite optical vegetation indices and LiDAR data have been used to this purpose. These two techniques are limited by cloud cover and are sensitive only to the top of vegetation. In addition, the vegetation attenuation to the soil microwave emission, represented by the vegetation optical depth (VOD), has been applied for biomass estimation using frequencies ranging from 4 to 30¿GHz (C- to K-bands). Atmosphere is transparent to microwaves and their sensitivity to canopy layers depends on the frequency, with lower frequencies having greater penetration depths. In this regard, L-band VOD (1.4¿GHz) is expected to enhance the ability to estimate carbon stocks. This study compares the sensitivity of different VOD products (from L, C, and X-bands) and an optical vegetation index (EVI) to the above-ground carbon density (ACD). It quantifies the contribution of ACD and forest cover proportion to the VOD/EVI signals. The study is conducted in Peru, southern Colombia and Panama, where ACD maps have been derived from airborne LiDAR. Results confirm the enhanced sensitivity of L-band VOD to ACD when compared to higher frequency bands, and show that the sensitivity of all VOD bands decreases in the densest forests. ACD explains 34% and forest cover 30% of L-band VOD variance, and these proportions gradually decrease for EVI, C-, and X-band VOD, respectively. Results are consistent through different categories of altitude and carbon density. This pattern is found in most of the studied regions and in flooded forests. Results also show that C-, X-band VOD and EVI provide complementary information to L-band VOD, especially in flooded forests and in mountains, indicating that synergistic approaches could lead to improved retrievals in these regions. Although the assessment of vegetation carbon in the densest forests requires further research, results from this study support the use of new L-band VOD estimates for mapping the carbon of tropical forests.Peer ReviewedPostprint (author's final draft

Global Soil Moisture Patterns Observed by Space Borne Microwave Radiometers and Scatterometers

Within the scope of the upcoming launch of a new water related satellite mission (SMOS) a global evaluation study was performed on two available global soil moisture products. ERS scatterometer surface wetness data was compared to AMSR-E soil moisture data. This study pointed out a strong similarity between both products in sparse to moderate vegetated regions with an average correlation coefficient of 0.83. Low correlations were found in densely vegetated areas and deserts. The low values in the vegetated regions can be explained by the limited soil moisture retrieval capabilities over dense vegetation covers. Soil emission is attenuated by the canopy and tends to saturate the microwave signal with increasing vegetation density, resulting in a decreased sensor sensitivity to soil moisture variations. It is expected that the new low frequency satellite mission (SMOS) will obtain soil moisture products with a higher quality in these regions. The low correlations in the desert regions are likely due to volume scattering or to the dielectric dynamics within the soil. The volume scattering in dry soils causes a higher backscatter under very dry conditions than under conditions when the sub-surface soil layers are somewhat wet. In addition, at low moisture levels the dielectric constant has a reduced sensitivity in response to changes in the soil moisture content. At a global scale the spatial correspondence of both products is high and both products clearly distinguish similar regions with high seasonal and inter annual variations. Based on the global analyses we concluded that the quality of both products was comparable and in the sparse to moderate vegetated regions both products may be beneficial for large scale validation of SMOS soil moisture. Some limitations of the studied products are different, pointing to significant potential for combining both products into one superior soil moisture data set. © The Author(s) 2008

Variability of Arctic and North Atlantic sea ice: A combined analysis of model results and observations from 1978 to 2001

Ice cover data simulated by a coupled sea ice-oceanmodel of the North Atlantic and the Arctic Ocean are compared withsatellite observations for the period 1978 to 2001. The capability ofthe model in reproducing the long-term mean state and the inter-seasonalvariability is demonstrated. The main modes of variability of thesatellite data and the simulation in the summer and winter half yearsare highly similar.Using NCEP/NCAR reanalysis data and the results from the sea ice-oceanmodel, we describe the relationship with atmospheric and oceanicvariables for the first two modes of sea-ice concentration variabilityin winter and in summer. The first winter mode shows a time delayedresponse to the Arctic Oscillation due to advection of heatanomalies in the ocean. The second winter mode is dominated by anevent in the late 1990s that is characterized by anomalously highpressure over the eastern Arctic. The first summer mode isstrongly influenced by the Arctic Oscillation of the previouswinter. The second summer mode is caused by anomalous air temperaturein the Arctic. This mode shows a distinctive trend and is related to anice extent reduction of about 4 10^5 km^2 over the 23 years ofanalysis

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