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Emma M. A. Dodd, Christopher J. Merchant, Nick A. Rayner, and Colin P. Morice

Abstract

Time series of global and regional mean surface air temperature (SAT) anomalies are a common metric used to estimate recent climate change. Various techniques can be used to create these time series from meteorological station data. The degree of difference arising from using five different techniques, based on existing temperature anomaly dataset techniques, to estimate Arctic SAT anomalies over land and sea ice was investigated using reanalysis data as a test bed. Techniques that interpolated anomalies were found to result in smaller errors than noninterpolating techniques relative to the reanalysis reference. Kriging techniques provided the smallest errors in estimates of Arctic anomalies, and simple kriging was often the best kriging method in this study, especially over sea ice. A linear interpolation technique had, on average, root-mean-square errors (RMSEs) up to 0.55 K larger than the two kriging techniques tested. Noninterpolating techniques provided the least representative anomaly estimates. Nonetheless, they serve as useful checks for confirming whether estimates from interpolating techniques are reasonable. The interaction of meteorological station coverage with estimation techniques between 1850 and 2011 was simulated using an ensemble dataset comprising repeated individual years (1979–2011). All techniques were found to have larger RMSEs for earlier station coverages. This supports calls for increased data sharing and data rescue, especially in sparsely observed regions such as the Arctic.

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A. K. Pavlov, A. Meyer, A. Rösel, L. Cohen, J. King, P. Itkin, J. Negrel, S. Gerland, S. R. Hudson, P. A. Dodd, L. de Steur, S. Mathisen, N. Cobbing, and M. A. Granskog

Abstract

Effective science communication is essential to share knowledge and recruit the next generation of researchers. Science communication to the general public can, however, be hampered by limited resources and a lack of incentives in the academic environment. Various social media platforms have recently emerged, providing free and simple science communication tools to reach the public and young people especially, an audience often missed by more conventional outreach initiatives. While individual researchers and large institutions are present on social media, smaller research groups are underrepresented. As a small group of oceanographers, sea ice scientists, and atmospheric scientists at the Norwegian Polar Institute, we share our experience establishing, developing, and maintaining a successful Arctic science communication initiative (@oceanseaicenpi) on Instagram, Twitter, and Facebook. The initiative is run entirely by a team of researchers with limited time and financial resources. It has built a broad audience of more than 7,000 followers, half of which is associated with the team’s Instagram account. To our knowledge, @oceanseaicenpi is one of the most successful Earth sciences Instagram accounts managed by researchers. The initiative has boosted the alternative metric scores of our publications and helped participating researchers become better writers and communicators. We hope to inspire and help other research groups by providing some guidelines on how to develop and conduct effective science communication via social media.

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P. Zion Klos, John T. Abatzoglou, Alycia Bean, Jarod Blades, Melissa A. Clark, Megan Dodd, Troy E. Hall, Amanda Haruch, Philip E. Higuera, Joseph D. Holbrook, Vincent S. Jansen, Kerry Kemp, Amber Lankford, Timothy E. Link, Troy Magney, Arjan J. H. Meddens, Liza Mitchell, Brandon Moore, Penelope Morgan, Beth A. Newingham, Ryan J. Niemeyer, Ben Soderquist, Alexis A. Suazo, Kerri T. Vierling, Von Walden, and Chelsea Walsh

Abstract

Climate change is well documented at the global scale, but local and regional changes are not as well understood. Finer, local- to regional-scale information is needed for creating specific, place-based planning and adaption efforts. Here the development of an indicator-focused climate change assessment in Idaho is described. This interdisciplinary framework couples end users’ data needs with observed, biophysical changes at local to regional scales. An online statewide survey of natural resource professionals was conducted to assess the perceived impacts from climate change and determine the biophysical data needed to measure those impacts. Changes to water resources and wildfire risk were the highest areas of concern among resource professionals. Guided by the survey results, 15 biophysical indicator datasets were summarized that included direct climate metrics (e.g., air temperature) and indicators only partially influenced by climate (e.g., wildfire). Quantitative changes in indicators were determined using time series analysis from 1975 to 2010. Indicators displayed trends of varying likelihood over the analysis period, including increasing growing-season length, increasing annual temperature, increasing forest area burned, changing mountain bluebird and lilac phenology, increasing precipitation intensity, earlier center of timing of streamflow, and decreased 1 April snowpack; changes in volumetric streamflow, salmon migration dates, and stream temperature displayed the least likelihood. A final conceptual framework derived from the social and biophysical data provides an interdisciplinary case example useful for consideration by others when choosing indicators at local to regional scales for climate change assessments.

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Nick A. Rayner, Renate Auchmann, Janette Bessembinder, Stefan Brönnimann, Yuri Brugnara, Francesco Capponi, Laura Carrea, Emma M. A. Dodd, Darren Ghent, Elizabeth Good, Jacob L. Høyer, John J. Kennedy, Elizabeth C. Kent, Rachel E. Killick, Paul van der Linden, Finn Lindgren, Kristine S. Madsen, Christopher J. Merchant, Joel R. Mitchelson, Colin P. Morice, Pia Nielsen-Englyst, Patricio F. Ortiz, John J. Remedios, Gerard van der Schrier, Antonello A. Squintu, Ag Stephens, Peter W. Thorne, Rasmus T. Tonboe, Tim Trent, Karen L. Veal, Alison M. Waterfall, Kate Winfield, Jonathan Winn, and R. Iestyn Woolway

Abstract

Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.

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