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Benjamin I. Cook, Edward R. Cook, Kevin J. Anchukaitis, Paul C. Huth, John E. Thompson, and Shanan F. Smiley

1. Introduction Over the last several years, potential errors and biases in the instrumental records of land and ocean temperatures have highlighted the difficulty in accurately and precisely measuring variability in surface climate. Known challenges include station microclimate and siting ( Pielke et al. 2002 ; Gallo 2005 ; Vose et al. 2005b ), land-use/land-cover changes ( Bonfils et al. 2008 ), bias introduced by instrumentation ( Quayle et al. 1991 ; Lin and Hubbard 2004 ; Hubbard et al

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Kaya Kanemaru, Takuji Kubota, Toshio Iguchi, Yukari N. Takayabu, and Riko Oki

precipitation climate record from the 17-yr PR data. The PR data show a discontinuity in quality associated with the switching to redundant electronics (A-to-B event) in June 2009. In version 7 of the level-1 PR product, a difference in noise power between before and after the A-to-B event is found so that a change in signal-to-noise ratio is expected. In this work, the noise power and the received power of the B-side PR after the A-to-B event are artificially increased to match those of the A-side PR to

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Likun Wang, Xiangqian Wu, Mitch Goldberg, Changyong Cao, Yaping Li, and Seung-Hee Sohn

spectral sampling interval of 0.25 cm −1 , successfully launched on board the Meteorological Operational Satellite Programme’s MetOp-A in October 2006 with a 0930 descending node ( Klaes et al. 2007 ). While primarily designed to improve numerical weather predications, the AIRS and IASI have the potential to provide a long-term record of accurately calibrated spectral radiances for climate monitoring and other climate-related studies because their high spectral resolutions offer inherent advantages

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Dorothy K. Hall, Josefino C. Comiso, Nicolo E. DiGirolamo, Christopher A. Shuman, Jeffrey R. Key, and Lora S. Koenig

High Resolution Radiometer (AVHRR) sensors since August 1981. Decadal-scale surface temperatures of the Arctic from the AVHRR, including the AVHRR Polar Pathfinder (APP) and Extended APP (APP-x) datasets, have been developed and used to study 30-yr trends in surface temperature ( Maslanik et al. 1998 ; Fowler et al. 2002 ; Comiso et al. 2003 ; Wang and Key 2003 , 2005a ). To ensure consistent climate modeling, there is a need to extend these IST records using AVHRR, and also using independent

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Frank J. Wentz

has many examples of the application of these satellite retrievals to climate research ( Wentz and Schabel 2000 ; Wentz et al. 2000 ; Trenberth et al. 2005 ; Chelton and Wentz 2005 ; Mears et al. 2007 ; Wentz et al. 2007 ). This paper describes the steps required to realize the full potential of TMI for climate applications. These include achieving proper geolocation, radio frequency interference (RFI) mitigation, and sensor calibration. With respect to calibration, the major challenge is to

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Cheng-Zhi Zou and Haifeng Qian

resolutions both vertically and horizontally. AMSU-A has a total of 15 channels with 6 of them dedicated to measuring temperature profiles from the lower to upper stratosphere. Although these instruments were designed primarily for weather observations, due to continuity and global coverage, together they are the basis for an indispensable climate data record (CDR) for monitoring historical temperature changes from the lower to upper stratosphere. MSU channel 4 and its companion AMSU-A channel 9 were

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Nadia Smith, William L. Smith Sr., Elisabeth Weisz, and Henry E. Revercomb

sources of uncertainty that cause significant discrepancies among data records from different instruments and affect their continuity in space and time. Instruments on weather satellites, in both geostationary and sun-synchronous orbits, have made measurements of key Earth system parameters for more than 50 years. This capability (i.e., Earth observation from space) greatly contributes to the advancement of data-dependent domains such as climate science and weather forecasting. Much of what is known

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Edward Hanna, Trausti Jónsson, Jon Ólafsson, and Hedinn Valdimarsson

1. Introduction Following our comprehensive analysis of recent changes in Icelandic climate ( Hanna et al. 2004 ), here we present a long-overdue update of Icelandic sea surface temperature (SST) records (Part I) and in a subsequent paper compare them with coastal air-temperature series (Hanna et al. 2006, unpublished manuscript, hereafter Part II). Statistical and graphical comparisons of the two temperature sets enable us to assess long-term (monthly–multidecadal) air–sea interaction in this

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Chris Funk, Pete Peterson, Seth Peterson, Shraddhanand Shukla, Frank Davenport, Joel Michaelsen, Kenneth R. Knapp, Martin Landsfeld, Gregory Husak, Laura Harrison, James Rowland, Michael Budde, Alex Meiburg, Tufa Dinku, Diego Pedreros, and Nicholas Mata

directly combine satellite and station-based estimates of T max to produce routinely updated data to support the monitoring of temperature extremes. Here, we present such a product—the new Climate Hazards Center Infrared Temperature with Stations (CHIRTS max ) Climate Data Record (CDR). Monthly maximum temperatures are the focus of this study because isolating a terrestrial T max signal from potential cloud contamination was easier than isolating minimum temperatures, which are harder to

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Jase Bernhardt

1. Introduction Since the Neolithic revolution, and to the present day, short-term weather and longer-term climate have been ubiquitous in their impacts on human well-being, in no small part due to the sensitivity of agriculture to seasonal weather and climate trends ( Butzer and Endfield 2012 ). Drought and resultant famine have impacted agricultural output and human civilizations throughout recorded history, from ancient Egypt ( Bernhardt et al. 2012 ) to more recent times ( Ladurie 1988

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