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A review of many studies published since the late 1920s reveals that the main driving mechanisms responsible for the early-twentieth-century Arctic warming (ETCAW) are not fully recognized. The main obstacle seems to be our limited knowledge about the climate of this period and some forcings. A deeper knowledge based on greater spatial and temporal resolution data is needed. The article provides new (or improved) knowledge about surface air temperature (SAT) conditions (including their extreme states) in the Arctic during the ETCAW. Daily and subdaily data have been used (mean daily air temperature, maximum and minimum daily temperature, and diurnal temperature range). These were taken from 10 individual years (selected from the period 1934–50) for six meteorological stations representing parts of five Arctic climatic regions. Standard SAT characteristics were analyzed (monthly, seasonal, and yearly means), as were rarely investigated aspects of SAT characteristics (e.g., number of characteristic days, day-to-day temperature variability, and the onset, end, and duration of thermal seasons). The results were compared with analogical calculations done for data taken from the contemporary Arctic warming (CAW) period (2007–16). The Arctic experienced warming between the ETCAW and the CAW. The magnitude of warming was greatest in the Pacific (2.7°C) and Canadian Arctic (1.9°C) regions. A shortening of winter and lengthening of summer were noted. Furthermore, the climate was also a little more continental (except the Russian Arctic) and less stable (greater day-to-day variability and diurnal temperature range) during the ETCAW than during the CAW. Significance Statement: It is well established that human activity (particularly increased greenhouse gas emissions) is the primary driving mechanism of the recent dramatic warming in the Arctic. However, the causes of a similar warming here in the first half of the twentieth century remain uncertain. The limited knowledge about the climate of that period—which mainly results from the low resolution of data—is a significant obstacle to a definitive determination of the forcing mechanisms. Therefore, the main aim of our paper is to improve our understanding of specific aspects of weather and climate (including extremes) using long-term series of daily and subdaily data that have rarely been applied for this purpose. This new, more comprehensive knowledge about the historical Arctic climate should allow the scientific community (particularly climate modelers) to better validate both climate models and reanalysis products and, consequently, to more precisely identify the causes of the early-twentieth-century Arctic warming. [ABSTRACT FROM AUTHOR]

This paper addresses the specter of a September ice-free Arctic in the 21st century using newly available simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We find that large spread in the projected timing of the September ice-free Arctic in 30 CMIP5 models is associated at least as much with different atmospheric model components as with initial conditions. Here we reduce the spread in the timing of an ice-free state using two different approaches for the 30 CMIP5 models: (i) model selection based on the ability to reproduce the observed sea ice climatology and variability since 1979 and (ii) constrained estimation based on the strong and persistent relationship between present and future sea ice conditions. Results from the two approaches show good agreement. Under a high-emission scenario both approaches project that September ice extent will drop to ~ 1.7 million km2 in the mid 2040s and reach the ice-free state (defined as 1 million km2) in 2054-2058. Under a medium-mitigation scenario, both approaches project a decrease to ~1.7 million km2 in the early 2060s, followed by a leveling off in the ice extent. [ABSTRACT FROM AUTHOR]

A better understanding of the interannual variability in temperature and precipitation datasets used as forcing fields for hydrologic models will lead to a more complete description of hydrologic model uncertainty, in turn helping scientists study the larger goal of how the Arctic terrestrial systemis responding to global change. Accordingly, this paper investigates temporal and spatial variability in monthly mean (1992--2000) temperature and precipitation datasets over the Western Arctic Linkage Experiment (WALE) study region. The six temperature datasets include 1) the fifth-generation Pennsylvania State University--National Center for Atmospheric Research MesoscaleModel (MM5); 2) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); 3) the Advanced Polar Pathfinderall-sky temperatures (APP); 4) National Centers for Environmental Prediction--National Center for Atmospheric Research (NCEP--NCAR) reanalyses (NCEP1); 5) the Climatic Research Unit/University of East Anglia CRUTEM2v (CRU); and 6) the Matsuura and Wilmott 0.5° × 0.5° Global Surface Air Temperature and Precipitation (MW). Comparisons of monthly precipitation are examined for MM5, ERA-40, NCEP1, CRU, and MW. Results of the temporal analyses indicate significant differences between at least two datasets (for either temperature or precipitation) in almost every month. The largest number of significant differences for temperature occurs in October, when there are five separate groupings; for precipitation, there are four significantly different groupings from March through June, and again in December. Spatial analyses of June temperatures indicate that the greatest dissimilarity is concentrated in the central portion of the study region, with the NCEP1 and APP datasets showing the greatest differences. In comparison, the spatial analysis of June precipitation datasets suggests that the largest dissimilarity is concentrated in the eastern portion of the study region. These results indicate that the choice of forcing datasets likely will have a significant effecton the output from hydrologic models, and several different datasets should beused for a robust hydrologic assessment. [ABSTRACT FROM AUTHOR]