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Peter W. Thorne
,
David E. Parker
,
John R. Christy
, and
Carl A. Mears

Historically, meteorological observations have been made for operational forecasting rather than long-term monitoring purposes, so that there have been numerous changes in instrumentation and procedures. Hence to create climate quality datasets requires the identification, estimation, and removal of many nonclimatic biases from the historical data. Construction of a number of new tropospheric temperature climate datasets has highlighted previously unrecognized uncertainty in multidecadal temperature trends aloft. The choice of dataset can even change the sign of upper-air trends relative to those reported at the surface. So structural uncertainty introduced unintentionally through dataset construction choices is important and needs to be understood and mitigated. A number of ways that this could be addressed for historical records are discussed, as is the question of How it needs to be reduced through future coordinated observing systems with long-term monitoring as a driver, enabling explicit calculation, and removal of nonclimatic biases. Although upper-air temperature records are used to illustrate the arguments, it is strongly believed that the findings are applicable to all long-term climate datasets and variables. A full characterization of observational uncertainty is as vitally important as recent intensive efforts to understand climate model uncertainties if the goal to rigorously reduce the uncertainty regarding both past and future climate changes is to be achieved.

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Christopher C. Hennon
,
Kenneth R. Knapp
,
Carl J. Schreck III
,
Scott E. Stevens
,
James P. Kossin
,
Peter W. Thorne
,
Paula A. Hennon
,
Michael C. Kruk
,
Jared Rennie
,
Jean-Maurice Gadéa
,
Maximilian Striegl
, and
Ian Carley

Abstract

The global tropical cyclone (TC) intensity record, even in modern times, is uncertain because the vast majority of storms are only observed remotely. Forecasters determine the maximum wind speed using a patchwork of sporadic observations and remotely sensed data. A popular tool that aids forecasters is the Dvorak technique—a procedural system that estimates the maximum wind based on cloud features in IR and/or visible satellite imagery. Inherently, the application of the Dvorak procedure is open to subjectivity. Heterogeneities are also introduced into the historical record with the evolution of operational procedures, personnel, and observing platforms. These uncertainties impede our ability to identify the relationship between tropical cyclone intensities and, for example, recent climate change.

A global reanalysis of TC intensity using experts is difficult because of the large number of storms. We will show that it is possible to effectively reanalyze the global record using crowdsourcing. Through modifying the Dvorak technique into a series of simple questions that amateurs (“citizen scientists”) can answer on a website, we are working toward developing a new TC dataset that resolves intensity discrepancies in several recent TCs. Preliminary results suggest that the performance of human classifiers in some cases exceeds that of an automated Dvorak technique applied to the same data for times when the storm is transitioning into a hurricane.

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Howard J. Diamond
,
Thomas R. Karl
,
Michael A. Palecki
,
C. Bruce Baker
,
Jesse E. Bell
,
Ronald D. Leeper
,
David R. Easterling
,
Jay H. Lawrimore
,
Tilden P. Meyers
,
Michael R. Helfert
,
Grant Goodge
, and
Peter W. Thorne

The year 2012 marks a decade of observations undertaken by the U.S. Climate Reference Network (USCRN) under the auspices of NOAA's National Climatic Data Center and Atmospheric Turbulence and Diffusion Division. The network consists of 114 sites across the conterminous 48 states, with additional sites in Alaska and Hawaii. Stations are installed in open (where possible), rural sites very likely to have stable land-cover/use conditions for several decades to come. At each site a suite of meteorological parameters are monitored, including triple redundancy for the primary air temperature and precipitation variables and for soil moisture/temperature. Instrumentation is regularly calibrated to National Institute for Standards and Technology (NIST) standards and maintained by a staff of expert engineers. This attention to detail in USCRN is intended to ensure the creation of an unimpeachable record of changes in surface climate over the United States for decades to come. Data are made available without restriction for all public, private, and government use. This article describes the rationale for the USCRN, its implementation, and some of the highlights of the first decade of operations. One critical use of these observations is as an independent data source to verify the existing U.S. temperature record derived from networks corrected for nonhomogenous histories. Future directions for the network are also discussed, including the applicability of USCRN approaches for networks monitoring climate at scales from regional to global. Constructive feedback from end users will allow for continued improvement of USCRN in the future and ensure that it continues to meet stakeholder requirements for precise climate measurements.

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Sarah J. Doherty
,
Stephan Bojinski
,
Ann Henderson-Sellers
,
Kevin Noone
,
David Goodrich
,
Nathaniel L. Bindoff
,
John A. Church
,
Kathy A. Hibbard
,
Thomas R. Karl
,
Lucka Kajfez-Bogataj
,
Amanda H. Lynch
,
David E. Parker
,
I. Colin Prentice
,
Venkatachalam Ramaswamy
,
Roger W. Saunders
,
Mark Stafford Smith
,
Konrad Steffen
,
Thomas F. Stocker
,
Peter W. Thorne
,
Kevin E. Trenberth
,
Michel M. Verstraete
, and
Francis W. Zwiers

The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) concluded that global warming is “unequivocal” and that most of the observed increase since the mid-twentieth century is very likely due to the increase in anthropogenic greenhouse gas concentrations, with discernible human influences on ocean warming, continental-average temperatures, temperature extremes, wind patterns, and other physical and biological indicators, impacting both socioeconomic and ecological systems. It is now clear that we are committed to some level of global climate change, and it is imperative that this be considered when planning future climate research and observational strategies. The Global Climate Observing System program (GCOS), the World Climate Research Programme (WCRP), and the International Geosphere-Biosphere Programme (IGBP) therefore initiated a process to summarize the lessons learned through AR4 Working Groups I and II and to identify a set of high-priority modeling and observational needs. Two classes of recommendations emerged. First is the need to improve climate models, observational and climate monitoring systems, and our understanding of key processes. Second, the framework for climate research and observations must be extended to document impacts and to guide adaptation and mitigation efforts. Research and observational strategies specifically aimed at improving our ability to predict and understand impacts, adaptive capacity, and societal and ecosystem vulnerabilities will serve both purposes and are the subject of the specific recommendations made in this paper.

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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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