We present a European land-only daily high-resolution gridded data set for precipitation and minimum, maximum, and mean surface temperature for the period 1950-2006. This data set improves on previous products in its spatial resolution and extent, time period, number of contributing stations, and attention to finding the most appropriate method for spatial interpolation of daily climate observations. The gridded data are delivered on four spatial resolutions to match the grids used in previous products as well as many of the rotated pole Regional Climate Models (RCMs) currently in use. Each data set has been designed to provide the best estimate of grid box averages rather than point values to enable direct comparison with RCMs. We employ a three-step process of interpolation, by first interpolating the monthly precipitation totals and monthly mean temperature using three-dimensional thin-plate splines, then interpolating the daily anomalies using indicator and universal kriging for precipitation and kriging with an external drift for temperature, then combining the monthly and daily estimates. Interpolation uncertainty is quantified by the provision of daily standard errors for every grid square. The daily uncertainty averaged across the entire region is shown to be largely dependent on the season and number of contributing observations. We examine the effect that interpolation has on the magnitude of the extremes in the observations by calculating areal reduction factors for daily maximum temperature and precipitation events with return periods up to 10 years. Copyright 2008 by the American Geophysical Union.

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A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006

Changes in climate extremes and their impacts on the natural physical environment: An overview of the IPCC SREX report

Precipitation downscaling improves the coarse resolution and poor representation of precipitation in global climate models and helps end users to assess the likely hydrological impacts of climate change. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user (in particular, impact modeler) needs and to discuss downscaling capabilities and gaps. End users need a reliable representation of precipitation intensities and temporal and spatial variability, as well as physical consistency, independent of region and season. In addition to presenting dynamical downscaling, we review perfect prognosis statistical downscaling, model output statistics, and weather generators, focusing on recent developments to improve the representation of space-time variability. Furthermore, evaluation techniques to assess downscaling skill are presented. Downscaling adds considerable value to projections from global climate models. Remaining gaps are uncertainties arising from sparse data; representation of extreme summer precipitation, subdaily precipitation, and full precipitation fields on fine scales; capturing changes in small-scale processes and their feedback on large scales; and errors inherited from the driving global climate model.

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Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user

Indices for climate variability and extremes have been used for a long time, often by assessing days with temperature or precipitation observations above or below specific physically-based thresholds. While these indices provided insight into local conditions, few physically based thresholds have relevance in all parts of the world. Therefore, indices of extremes evolved over time and now often focus on relative thresholds that describe features in the tails of the distributions of meteorological variables. In order to help understand how extremes are changing globally, a subset of the wide range of possible indices is now being coordinated internationally which allows the results of studies from different parts of the world to fit together seamlessly. This paper reviews these as well as other indices of extremes and documents the obstacles to robustly calculating and analyzing indices and the methods developed to overcome these obstacles. Gridding indices are necessary in order to compare observations with climate model output. However, gridding indices from daily data are not always straightforward because averaging daily information from many stations tends to dampen gridded extremes. The paper describes recent progress in attribution of the changes in gridded indices of extremes that demonstrates human influence on the probability of extremes. The paper also describes model projections of the future and wraps up with a discussion of ongoing efforts to refine indices of extremes as they are being readied to contribute to the IPCC's Fifth Assessment Report. WIREs Clim Change 2011, 2:851–870. doi: 10.1002/wcc.147

For further resources related to this article, please visit the WIREs website.

Indices for monitoring changes in extremes based on daily temperature and precipitation data

[1] This paper provides a first overview of the performance of state-of-the-art global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating climate extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), and compares it to that in the previous model generation (CMIP3). For the first time, the indices based on daily temperature and precipitation are calculated with a consistent methodology across multimodel simulations and four reanalysis data sets (ERA40, ERA-Interim, NCEP/NCAR, and NCEP-DOE) and are made available at the ETCCDI indices archive website. Our analyses show that the CMIP5 models are generally able to simulate climate extremes and their trend patterns as represented by the indices in comparison to a gridded observational indices data set (HadEX2). The spread amongst CMIP5 models for several temperature indices is reduced compared to CMIP3 models, despite the larger number of models participating in CMIP5. Some improvements in the CMIP5 ensemble relative to CMIP3 are also found in the representation of the magnitude of precipitation indices. We find substantial discrepancies between the reanalyses, indicating considerable uncertainties regarding their simulation of extremes. The overall performance of individual models is summarized by a “portrait” diagram based on root-mean-square errors of model climatologies for each index and model relative to four reanalyses. This metric analysis shows that the median model climatology outperforms individual models for all indices, but the uncertainties related to the underlying reference data sets are reflected in the individual model performance metrics.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrd.50203

Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate

The European Climate Assessment (ECA) dataset of daily observations, which has been widely used for studies on climate extremes, has been updated and extended. It now contains observational series of 2191 stations located in Europe and the Mediterranean (average inter-station distance: ∼75 km). About 1200 precipitation series and 750 temperature series cover the period 1960–2000. For a small number of stations (<15%) air pressure, cloud cover, sunshine duration, snow depth and relative humidity series have been collected. All series are quality controlled and the homogeneity of the precipitation and temperature series is assessed. About 50% of the daily series are publicly available for climate studies through the website http://eca.knmi.nl. The main potential of the ECA dataset follows from its daily resolution, enabling studies of impact relevant climate extremes and variability. To guide these studies, climate indices calculated from the ECA series are presented on the website too. Besides, gridded versions of the daily ECA data are available for easy comparison with climate model simulations. A trend analysis for the diurnal temperature range (DTR) demonstrates the utility of the dataset. Seasonal and annual DTR trends were calculated for 333 homogeneous temperature series in ECA and a Europe average trend was estimated. In spring and summer, the DTR increased from 1979 to 2005, whereas in autumn and winter the DTR generally decreased. The European average trend in annual DTR was 0.09 °C decade−1. Copyright © 2008 Royal Meteorological Society

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E. J. Klok

Papers

A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006

Updated and extended European dataset of daily climate observations