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In recent years, numerous studies have investigated the relationship of extreme precipitation intensities with temperature derived from a binning method using present‐day weather data. The aim of this approach is that these dependencies, or so‐called apparent scaling rates, provide insights on how precipitation extremes could evolve in the future climate. In a recent paper, Bao et al. showed that there is a large discrepancy between the present‐day apparent scaling rates—showing a negative dependency of daily rainfall extremes on temperature for Darwin—and the climate change response showing a substantial increase in daily rainfall extremes. Their results provide compelling evidence that it may not be straightforward to predict the climate change response from the temperature scaling. We agree with this statement, but disagree with the physical explanation put forward in that study and think that moisture limitations are the most likely cause. Therefore, we advocate the use of dew point temperatures in scaling studies as a much more direct proxy of the absolute humidity of the air in which precipitation extremes develop, which we think is physically more consistent with the underlying mechanism causing the increase in precipitation extremes with global warming. [ABSTRACT FROM AUTHOR]

This paper compares extreme value statistics of daily maximum 10 m wind speed in winter simulated by the regional climate model COSMO‐CLM at a horizontal resolution of 15 km (C15) with the reanalyses ERA‐Interim and Arctic System reanalysis (ASR version 1 and 2) and with a satellite data set (CCMPv2). Our C15 simulation (1979/1980–2015/2016, November–April) is thereby the longest high‐resolution simulation available for the Arctic. The results show that the extreme wind speeds tend to increase over the ocean with increasing the horizontal model resolution. A horizontal resolution of ≤15 km is required to sufficiently capture all extreme wind characteristics, in particular for the tip jets and barrier winds over the Irminger Sea and in the Denmark Strait, and for the low‐level jets in the Nares Strait. Of almost equal importance are physical parameterizations of surface fluxes and of turbulence. Capturing extreme wind characteristics has a direct effect on climate relevant air–ice–ocean interactions, such as triggering open‐ocean deep convection, polynya dynamics, or on the sea ice and freshwater balance of the Arctic. State‐of‐the‐art global climate models and reanalyses (e.g., ERA‐Interim) underestimate the extreme near‐surface wind speeds in the Arctic and around Greenland. Regional climate models, for example, ASR/WRF or COSMO‐CLM, resolve the mesoscale wind systems that frequently produce extreme winds, which we estimated i.a. as 10‐year return levels (see the figure). Capturing extreme wind characteristics has a direct effect on climate relevant air–ice–ocean interactions, such as triggering open‐ocean deep convection, polynya dynamics, or on the sea ice and freshwater balance of the Arctic. [ABSTRACT FROM AUTHOR]

The European Centre for Medium‐Range Weather Forecasts (ECMWF) has recently finished ERA‐40, a reanalysis covering the period September 1957 to August 2002. One of the products of ERA‐40 consists of six‐hourly global fields of wave parameters, like significant wave height and wave period. These data have been generated with the centre's WAM wave model. From these results we have derived climatologies of important wave parameters, including significant wave height, mean wave period, and extreme significant wave heights. Particular emphasis is on the variability of these parameters, both in space and time. Besides being important for scientists studying climate change, these results are also important for engineers who have to design maritime constructions. This paper describes the ERA‐40 data and gives an overview of the results derived. The results are available on a global 1.5° × 1.5° grid. They are accessible from the Web‐based KNMI/ERA‐40 wave atlas at http://www.knmi.nl/waveatlas. Copyright © 2005 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The "Warm‐Arctic/Cold‐continents" (WACC) winter weather pattern is investigated using the European Centre for Medium‐Range Weather Forecasts 20th Century reanalysis data (ERA20C) spanning 1901–2010. Both the 1920–1940 and 1990–2010 periods are characterized by Arctic amplification (AA) and mid‐latitude continental cooling, although the Arctic warming signal for 1990–2010 is twice as strong as that for 1920–1940. Significant weakening in the mid‐latitude polewards temperature gradient and zonal wind, wavier upper‐level flow character, and strong regional blocking frequency/intensity changes are also detected during both AA periods. These results based on statistical analyses highlight the possible role of AA in affecting mid‐latitude weather patterns, but further work is needed to quantify the influence of AA on particular mid‐latitude dynamical features. "Warm‐Arctic/Cold‐continents" (WACC) winter weather pattern is clearly shown in both 1920–1940 and 1990–2010 periods. Significant weakening in mid‐latitude polewards temperature gradient and zonal wind, wavier upper‐level flow character, and strong regional blocking frequency/intensity changes are detected during both periods. Noticeable regional differences of trends in these dynamical features between AA1 and AA2 period suggest the need for a more in‐depth dynamical meteorological investigation of regional mechanisms of AA and mid‐latitude weather linkages. [ABSTRACT FROM AUTHOR]

Inhomogeneities in climate data are the main source of uncertainty for secular warming estimates. To reduce the influence of inhomogeneities in station data statistical homogenization compares a candidate station to its neighbours to detect and correct artificial changes in the candidate. Many studies have quantified the performance of statistical break detection tests used in this comparison. Also, full homogenization methods have been studied numerically, but correction methods by themselves have not been studied much. We analyse the ANOVA (analysis of variance) joint correction method, which is expected to be the most accurate published method. We find that, if all breaks are known, this method produces unbiased trend estimates and that in this case the uncertainty in the trend estimates is not determined by the variance of the inhomogeneities, but by the variance of the weather and measurement noise. For low signal‐to‐noise ratios and high numbers of breaks, the correction may also worsen the data by increasing the original random unbiased trend error. Any uncertainty in the break dates leads to a systematic undercorrection of the trend errors and in this more realistic case the variance of the inhomogeneities is also important. Inhomogeneities in climate station data may produce spurious temperature trends. After the detection of the breaks the data have to be adjusted by a correction scheme. We analyse simulated data with known properties to assess their skill. The scatterplot compares the trend error before and after the adjustment. The original trend error is largely reduced, but not completely removed. [ABSTRACT FROM AUTHOR]

Climate inter‐annual variability over the North American monsoon (NAM) region is associated with El Niño‐Southern Oscillation (ENSO) and Pacific decadal variability (PDV), which drive a warm season atmospheric teleconnection response. Using the North American Regional Climate Change Assessment Program (NARCCAP) simulations, previous studies have found that regional models forced with an atmospheric reanalysis (NARCCAP Phase I) represent the NAM reasonably well as a climatological feature. However, when these same regional models are forced with global climate model projections (NARCCAP Phase II), their ability to represent the NAM as a salient feature substantially degrades. The present study evaluates NAM inter‐annual climate variability through the continental‐scale patterns of summer precipitation within the NARCCAP simulations (Phases I and II), in relation to ENSO–PDV, and the presence of the driving atmospheric teleconnection response. Multivariate statistical analyses are applied to sea surface temperature and precipitation data sets to determine dominant variability at continental scale, with focus on the southwest. The analysis reveals that NARCCAP Phase I simulations are able to portray the spatial pattern of precipitation associated with ENSO–PDV in a similar way to observations. However, all NARCCAP Phase II simulations, with the exception of the HRM(Hadcm3) regional–global model pair, fail to reproduce this climate variability. Although including all possible NARCCAP model simulations to generate a multi‐model ensemble mean would increase the statistical degree of confidence in climate projections, this type of result would not increase confidence in the physical climatology of model representations of warm season climate variability. More physically based, process‐oriented metrics are needed to evaluate model quality in assessing the uncertainty of future climate change in multi‐model ensemble products used for climate change impacts assessments. [ABSTRACT FROM AUTHOR]