Search Results

You are looking at 1 - 4 of 4 items for

  • Author or Editor: Richard C. Cornes x
  • Refine by Access: All Content x
Clear All Modify Search
Richard C. Cornes
,
Philip D. Jones
, and
Cheng Qian

Abstract

The annual cycle of surface air temperature is examined across Northern Hemisphere land areas (north of 25°N) by comparing the results from the Climatic Research Unit Time Series (CRU TS) dataset against four reanalysis datasets: two versions of the NOAA Twentieth Century Reanalysis (20CR and 20CRC) and two versions of the ECMWF Twentieth Century Reanalysis, version 2 (ERA-20C) and version 2c (ERA-20CM). The modulated annual cycle is adaptively derived from an ensemble empirical mode decomposition (EEMD) filter, and is used to define the phase and amplitude of the annual cycle. The EEMD method does not impose a simple sinusoidal shape of the annual cycle. None of the reanalysis simulations assimilates surface temperature or land-use data. However, they differ in the parameters that are included: both ERA-20C and 20CR assimilate surface pressure data; ERA-20C also includes surface wind data over the oceans; and ERA-20CM does not assimilate any of these synoptic data. It is demonstrated that synoptic variability is critical for explaining the trends and variability of the annual cycle of surface temperature across the Northern Hemisphere. The CMIP5 forcings alone are insufficient to explain the observed trends and decadal-scale variability, particularly with respect to the decline in the amplitude of the annual cycle throughout the twentieth century. The variability in the annual cycle during the latter half of the twentieth century was unusual in the context of the twentieth century, and was most likely related to large-scale atmospheric variability, although uncertainty in the results is greatest before about 1930.

Full access
Thomas E. Cropper
,
David I. Berry
,
Richard C. Cornes
, and
Elizabeth C. Kent

Abstract

Marine air temperatures recorded on ships during the daytime are known to be biased warm on average due to energy storage by the superstructure of the vessels. This makes unadjusted daytime observations unsuitable for many applications including for the monitoring of long-term temperature change over the oceans. In this paper a physics-based approach is used to estimate this heating bias in ship observations from ICOADS. Under this approach, empirically determined coefficients represent the energy transfer terms of a heat budget model that quantifies the heating bias and is applied as a function of cloud cover and the relative wind speed over individual ships. The coefficients for each ship are derived from the anomalous diurnal heating relative to nighttime air temperature. Model coefficients, cloud cover, and relative wind speed are then used to estimate the heating bias ship by ship and generate nighttime-equivalent time series. A variety of methodological approaches were tested. Application of this method enables the inclusion of some daytime observations in climate records based on marine air temperatures, allowing an earlier start date and giving an increase in spatial coverage compared to existing records that exclude daytime observations.

Significance Statement

Currently, the longest available record of air temperature over the oceans starts in 1880. We present an approach that enables observations of air temperatures over the oceans to be used in the creation of long-term climate records that are presently excluded. We do this by estimating the biases inherent in daytime temperature reports from ships, and adjust for these biases by implementing a numerical heat-budget model. The adjustment can be applied to the variety of ship types present in observational archives. The resulting adjusted temperatures can be used to create a more spatially complete record over the oceans, that extends further back in time, potentially into the late eighteenth century.

Open access
Antonello A. Squintu
,
Gerard van der Schrier
,
Else J. M. van den Besselaar
,
Richard C. Cornes
, and
Albert M. G. Klein Tank

ABSTRACT

Long and homogeneous series are a necessary requirement for reliable climate analysis. Relocation of measuring equipment from one station to another, such as from the city center to a rural area or a nearby airport, is one of the causes of discontinuities in these long series that may affect trend estimates. In this paper, an updated procedure for the composition of long series, by combining data from nearby stations, is introduced. It couples an evolution of the blending procedure already implemented within the European Climate Assessment and Dataset (ECA&D, which combines data from stations no more than 12.5 km apart from each other) with a duplicate removal, alongside the quantile matching homogenization procedure. The ECA&D contains approximately 3000 homogenized series for each temperature variable prior to the blending procedure, and approximately 820 of these are longer than 60 years; the process of blending increases the number of long series to more than 900. Three case studies illustrate the effects of the homogenization on single blended series, showing the effectiveness of separate adjustments on extreme and mean values (Geneva, Switzerland), on cases in which blending is complex (Rheinstetten, Germany), and on series that are completed by adding relevant portions of Global Telecommunications System synoptic data (Siauliai, Lithuania). A trend assessment on the whole European continent reveals the removal of negative and very large trends, demonstrating a stronger spatial consistency. The new blended and homogenized dataset will allow a more reliable use of temperature series for indices calculation and for the calculation of gridded datasets and it will be available online for users (https://www.ecad.eu).

Free access
Else J. M. van den Besselaar
,
Gerard van der Schrier
,
Richard C. Cornes
,
Aris Suwondo Iqbal
, and
Albert M. G. Klein Tank

Abstract

This study introduces a new daily high-resolution land-only observational gridded dataset, called SA-OBS, for precipitation and minimum, mean, and maximum temperature covering Southeast Asia. This dataset improves upon existing observational products in terms of the number of contributing stations, in the use of an interpolation technique appropriate for daily climate observations, and in making estimates of the uncertainty of the gridded data. The dataset is delivered on a 0.25° × 0.25° and a 0.5° × 0.5° regular latitude–longitude grid for the period 1981–2014. The dataset aims to provide best estimates of grid square averages rather than point values to enable direct comparisons with regional climate models. Next to the best estimates, daily uncertainties are quantified. The underlying daily station time series are collected in cooperation between meteorological services in the region: the Southeast Asian Climate Assessment and Dataset (SACA&D). Comparisons are made with station observations and other gridded station or satellite-based datasets (APHRODITE, CMORPH, TRMM). The comparisons show that vast differences exist in the average daily precipitation, the number of rainy days, and the average precipitation on a wet day between these datasets. SA-OBS closely resembles the station observations in terms of dry/wet frequency, the timing of precipitation events, and the reproduction of extreme precipitation. New versions of SA-OBS will be released when the station network in SACA&D has grown further.

Full access