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The Arctic region has exhibited dramatic changes in recent times. Many of these are intimately tied up with synoptic activity, but little research has been undertaken on how the characteristics of Arctic cyclones have changed. This paper presents a comprehensive analysis of Arctic (here defined as the domain north of 70°N) cyclones diagnosed with the Melbourne University cyclone tracking scheme applied to the 40-yr ECMWF Re-Analysis (ERA-40) and the NCEP–NCAR (NCEP1) and NCEP–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP)-II (NCEP2) reanalysis sets (the last two extending to the end of 2006). A wide variety of cyclone characteristics is presented as befits these complex features. In winter the highest density of cyclones is found between Norway and Svalbard and to the east to the Barents and Kara Seas, and significant numbers are found in the central Arctic. In summer the greatest frequencies are found in the central Arctic. The total number of cyclones identified in the ERA-40 record exceeds those in the two NCEP compilations. The mean size of cyclones shows similar maxima in the central Arctic in both winter and summer. By contrast, the greatest mean system depth in winter (in excess of 8 hPa) is found to the southeast of Greenland, although average depths exceed 6 hPa over a considerable portion of the basin. In summer the deepest cyclones are found in the central portion of the Arctic. The analysis shows that the total number of cyclones in winter exceeds that in summer, a result in contrast to earlier studies. This difference comes about primarily due to the greater numbers of “open strong” systems in winter in all reanalyses. Cyclones in this category are associated with very active synoptic situations; it is of importance that they be included in cyclone counts but would not be considered in many cyclone identification schemes. Since 1979 neither the ERA-40 nor the NCEP2 sets show significant trends in any of the cyclone variables considered. However, over the entire record starting in 1958 the NCEP1 reanalysis exhibits a significant increase in summer cyclone frequency (due mainly to the increase in closed strong systems). Both NCEP1 and ERA-40 also reveal significant increases in the number of summer closed strong cyclones, as well as in their mean depth and intensity in that season. Interannual variations in Arctic cyclone numbers are closely related to the Arctic Oscillation (AO) index in the full reanalyses records. An even stronger relationship is found between the AO and the number of deep cyclones. These relationships have still held in the last decade when the AO has returned to more normal values but the summer and fall sea ice extent has continued to decrease. [ABSTRACT FROM AUTHOR]

Downwelling radiation in six regional models from the Arctic Regional Climate Model Intercomparison (ARCMIP) project is systematically biased negative in comparison with observations from the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment, although the correlations with observations are relatively good. In this paper, links between model errors and the representation of clouds in these models are investigated. Although some modeled cloud properties, such as the cloud water paths, are reasonable in a climatological sense, the temporal correlation of model cloud properties with observations is poor. The vertical distribution of cloud water is distinctly different among the different models; some common features also appear. Most models underestimate the presence of high clouds, and, although the observed preference for low clouds in the Arctic is present in most of the models, the modeled low clouds are too thin and are displaced downward. Practically all models show a preference to locate the lowest cloud base at the lowest model grid point. In some models this happens also to be where the observations show the highest occurrence of the lowest cloud base; it is not possible to determine if this result is just a coincidence. Different factors contribute to model surface radiation errors. For longwave radiation in summer, a negative bias is present both for cloudy and clear conditions, and intermodel differences are smaller when clouds are present. There is a clear relationship between errors in cloud-base temperature and radiation errors. In winter, in contrast, clear-sky cases are modeled reasonably well, but cloudy cases show a very large intermodel scatter with a significant bias in all models. This bias likely results from a complete failure in all of the models to retain liquid water in cold winter clouds. All models overestimate the cloud attenuation of summer solar radiation for thin and intermediate clouds, and some models maintain this behavior also for thick clouds. [ABSTRACT FROM AUTHOR]

The state-of-the-art AOGCM simulations have recently (late 2004–early 2005) been completed for the Intergovernmental Panel on Climate Change (IPCC) in order to provide input to the IPCC’s Fourth Assessment Report (AR4). The present paper synthesizes the new simulations of both the twentieth- and twenty-first-century arctic freshwater budget components for use in the IPCC AR4, and attempts to determine whether demonstrable progress has been achieved since the late 1990s. Precipitation and its difference with evapotranspiration are addressed over the Arctic Ocean and its terrestrial watersheds, including the basins of the four major rivers draining into the Arctic Ocean: the Ob, the Yenisey, the Lena, and the Mackenzie. Compared to the previous [IPCC Third Assessment Report (TAR)] generation of AOGCMs, there are some indications that the models as a class have improved in simulations of the Arctic precipitation. In spite of observational uncertainties, the models still appear to oversimulate area-averaged precipitation over the major river basins. The model-mean precipitation biases in the Arctic and sub-Arctic have retained their major geographical patterns, which are at least partly attributable to the insufficiently resolved local orography, as well as to biases in large-scale atmospheric circulation and sea ice distribution. The river discharge into the Arctic Ocean is also slightly oversimulated. The simulated annual cycle of precipitation over the Arctic Ocean is in qualitative agreement between the models as well as with observational and reanalysis data. This is also generally the case for the seasonality of precipitation over the Arctic Ocean’s terrestrial watersheds, with a few exceptions. Some agreement is demonstrated by the models in reproducing positive twentieth-century trends of precipitation in the Arctic, as well as positive area-averaged P–E late-twentieth-century trends over the entire terrestrial watershed of the Arctic Ocean. For the twenty-first century, three scenarios are considered: A2, A1B, and B1. Precipitation over the Arctic Ocean and its watersheds increases through the twenty-first century, showing much faster percentage increases than the global mean precipitation. The arctic precipitation changes have a pronounced seasonality, with the strongest relative increase in winter and fall, and the weakest in summer. The river discharge into the Arctic Ocean increases for all scenarios from all major river basins considered, and is generally about twice as large as the increase of freshwater from precipitation over the Arctic Ocean (70°–90°N) itself. The across-model scatter of the precipitation increase for each scenario is significant, but smaller than the scatter between the climates of the different models in the baseline period. [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]

Attribution of extreme events is a challenging science and one that is currently undergoing considerable evolution. In this paper are 19 analyses by 18 different research groups, often using quite different methodologies, of 12 extreme events that occurred in 2012. In addition to investigating the causes of these extreme events, the multiple analyses of four of the events, the high temperatures in the United States, the record low levels of Arctic sea ice, and the heavy rain in northern Europe and eastern Australia, provide an opportunity to compare and contrast the strengths and weaknesses of the various methodologies. The differences also provide insights into the structural uncertainty of event attribution, that is, the uncertainty that arises directly from the differences in analysis methodology. In these cases, there was considerable agreement between the different assessments of the same event. However, different events had very different causes. Approximately half the analyses found some evidence that anthropogenically caused climate change was a contributing factor to the extreme event examined, though the effects of natural fluctuations of weather and climate on the evolution of many of the extreme events played key roles as well. [ABSTRACT FROM AUTHOR]