HadISDH land surface multi-variable humidity and temperature record for climate monitoring

Willett, K. M.; Dunn, Robert; Thorne, Peter; Bell, S A; Podesta, Michael de; Parker, D. E.; Jones, Phil; Williams, Claude N.

doi:10.5194/cp-10-1983-2014

Cited by 144 publications

(141 citation statements)

References 42 publications

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“…The trend in relative humidity is consistent with that seen in historical regional (Jenkins et al, 2008) and global (Dai, 2006;Willett et al, 2014) analyses. Although not statistically significant overall, it contributes to between 57 and 68 % of the trends in PET (between 39 and 46 % or the trends in spring PET).…”

Section: Discussionsupporting

confidence: 76%

“…Although not statistically significant overall, it contributes to between 57 and 68 % of the trends in PET (between 39 and 46 % or the trends in spring PET). Globally trends in relative humidity vary spatially, with mid-latitudes showing a decrease and the tropics and high-latitudes showing an increase, despite an overall increase in specific humidity over land, particularly in the Northern Hemisphere (Dai, 2006;Willett et al, 2014). In these global analyses, Great Britain is in a region of transition between decreasing relative humidity in western Europe and increasing relative humidity in Scandinavia, so that small decreasing trends are found, but they are not significant; this is consistent with our findings.…”

Section: Discussionmentioning

confidence: 99%

“…While there are overall global trends, the impacts can vary between regions (IPCC, 2014b). In the UK, wildlife surveys of both flora (Wood et al, 2015;Evans et al, 2008) and fauna (Pocock et al, 2015) show a shift in patterns and timing (Thackeray et al, 2010). In addition, the UK natural resources of freshwater (Watts et al, 2015), soils (Reynolds et al, 2013;Bellamy et al, 2005) and vegetation (Berry et al, 2002;Hickling et al, 2006;Norton et al, 2012) are changing.…”

Section: Introductionmentioning

confidence: 99%

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Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data

Robinson

Blyth

Clark

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Observations of climate are often available on very different spatial scales from observations of the natural environments and resources that are affected by climate change. In order to help bridge the gap between these scales using modelling, a new dataset of daily meteorological variables was created at 1 km resolution over Great Britain for the years 1961-2012, by interpolating coarser resolution climate data and including the effects of local topography. These variables were used to calculate atmospheric evaporative demand (AED) at the same spatial and temporal resolution. Two functions that represent AED were chosen: one is a standard form of potential evapotranspiration (PET) and the other is a derived PET measure used by hydrologists that includes the effect of water intercepted by the canopy (PETI). Temporal trends in these functions were calculated, with PET found to be increasing in all regions, and at an overall rate of 0.021 ± 0.021 mm day −1 decade −1 in Great Britain. PETI was found to be increasing at a rate of 0.019 ± 0.020 mm day −1 decade −1 in Great Britain, but this was not statistically significant. However, there was a trend in PETI in England of 0.023 ± 0.023 mm day −1 decade −1 . The trends were found to vary by season, with spring PET increasing by 0.043 ± 0.019 mm day −1 decade −1 (0.038 ± 0.018 mm day −1 decade −1 when the interception correction is included) in Great Britain, while there is no statistically significant trend in other seasons. The trends were attributed analytically to trends in the climate variables; the overall positive trend was predominantly driven by rising air temperature, although rising specific humidity had a negative effect on the trend. Recasting the analysis in terms of relative humidity revealed that the overall effect is that falling relative humidity causes the PET to rise. Increasing downward short-and longwave radiation made an overall positive contribution to the PET trend, while decreasing wind speed made a negative contribution to the trend in PET. The trend in spring PET was particularly strong due to a strong decrease in relative humidity and increase in downward shortwave radiation in the spring.

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Section: Discussionsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data

Robinson

Blyth

Clark

et al. 2017

Hydrol. Earth Syst. Sci.

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“…The formulae we use are the same as in HadISDH (see Willett et al, 2014 for full details) but we give the method here with the specific formulae in Table 5. Firstly the subdaily sea-level pressure values provided in HadISD are converted to station-level pressure using the formula from List (1963).…”

Section: Data Provisionmentioning

confidence: 99%

Expanding HadISD: quality-controlled, sub-daily station data from 1931

Dunn

Willett

Parker

et al. 2016

Geosci. Instrum. Method. Data Syst.

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Abstract. HadISD is a sub-daily, station-based, qualitycontrolled dataset designed to study past extremes of temperature, pressure and humidity and allow comparisons to future projections. Herein we describe the first major update to the HadISD dataset. The temporal coverage of the dataset has been extended to 1931 to present, doubling the time range over which data are provided. Improvements made to the station selection and merging procedures result in 7677 stations being provided in version 2.0.0.2015p of this dataset. The selection of stations to merge together making composites has also been improved and made more robust. The underlying structure of the quality control procedure is the same as for HadISD.1.0.x, but a number of improvements have been implemented in individual tests. Also, more detailed quality control tests for wind speed and direction have been added. The data will be made available as NetCDF files at http://www.metoffice.gov.uk/hadobs/hadisd and updated annually.

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“…There are a number of ways of characterising the amount of water vapour present in the air (see Willett et al, 2014a for examples), but in this report we will focus on relative and specific humidity. The relative humidity is the amount of water present expressed as a fraction of the amount that would be present if the air were saturated.…”

Section: Introductionmentioning

confidence: 99%

Comparison of land surface humidity between observations and CMIP5 models

Dunn¹,

Willett²,

Ciavarella³

et al. 2017

Earth Syst. Dynam.

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Abstract.We compare the latest observational land surface humidity dataset, HadISDH, with the latest generation of climate models extracted from the CMIP5 archive and the ERA-Interim reanalysis over the period 1973 to present. The globally averaged behaviour of HadISDH and ERA-Interim are very similar in both humidity measures and air temperature, on decadal and interannual timescales.The global average relative humidity shows a gradual increase from 1973 to 2000, followed by a steep decline in recent years. The observed specific humidity shows a steady increase in the global average during the early period but in the later period it remains approximately constant. None of the CMIP5 models or experiments capture the observed behaviour of the relative or specific humidity over the entire study period. When using an atmosphere-only model, driven by observed sea surface temperatures and radiative forcing changes, the behaviour of regional average temperature and specific humidity are better captured, but there is little improvement in the relative humidity.Comparing the observed climatologies with those from historical model runs shows that the models are generally cooler everywhere, are drier and less saturated in the tropics and extra-tropics, and have comparable moisture levels but are more saturated in the high latitudes. The spatial pattern of linear trends is relatively similar between the models and HadISDH for temperature and specific humidity, but there are large differences for relative humidity, with less moistening shown in the models over the tropics and very little at high latitudes. The observed drying in mid-latitudes is present at a much lower magnitude in the CMIP5 models. Relationships between temperature and humidity anomalies (T -q and T -rh) show good agreement for specific humidity between models and observations, and between the models themselves, but much poorer for relative humidity. The T -q correlation from the models is more steeply positive than the observations in all regions, and this over-correlation may be due to missing processes in the models.The observed temporal behaviour appears to be a robust climate feature rather than observational error. It has been previously documented and is theoretically consistent with faster warming rates over land compared to oceans. Thus, the poor replication in the models, especially in the atmosphere-only model, leads to questions over future projections of impacts related to changes in surface relative humidity. It also precludes any formal detection and attribution assessment.

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HadISDH land surface multi-variable humidity and temperature record for climate monitoring

Cited by 144 publications

References 42 publications

Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data

Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data

Expanding HadISD: quality-controlled, sub-daily station data from 1931

Comparison of land surface humidity between observations and CMIP5 models

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