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HadISDH - gridded global surface humidity dataset

HadISDH is a global gridded monthly mean surface humidity dataset. Quality controlled and homogenised / bias adjusted monthly mean anomalies (relative to a 1991-2020 base period) are provided alongside uncertainty estimates (observation and gridbox sampling). Actual values, climatological mean and standard deviation and no. observations are also provided. The dataset begins in January 1973 and is updated annually.

HadISDH.land is a global gridded monthly mean land surface humidity dataset based on the quality controlled sub-daily HadISD dataset which is in turn based on the ISD dataset from NOAA's NCEI. Hourly dew poinbt temperature and air temperature are converted to various humidity variables and then averaged to monthly values. These are homogenised and averaged over 5° by 5° degree gridboxes for each month.

HadISDH.marine is a global gridded monthly mean ocean surface humidity dataset. Hourly in situ dew point temperature and marine air temperature data from ships are taken from ICOADS. These are then converted to various humidity variables, quality controlled, bias adjusted and averaged over 5° by 5° degree gridboxes for each month.

HadISDH.blend is a global gridded monthly mean land and marine surface humidity dataset combining HadISDH.land and HadISDH.marine.

HadISDH.extremes is a new global gridded monthly mean land surface humidity dataset. It builds upon HadISDH.land providing a monthly gridded product of wet and dry bulb temperature extremes indices for monitoring heat extremes over land.


LATEST VERSIONS:
HadISDH.land.4.5.1.2022f covers January 1973 to December 2022. Update Document.
HadISDH.marine.1.4.1.2022f covers January 1973 to December 2022. Update Document.
HadISDH.blend.1.4.1.2022f covers January 1973 to December 2022.
HadISDH.extremes.1.0.0.2022f covers January 1973 to December 2022. Update Document.
For previous versions please contact the dataset maintainers.


Gridded products are available for 6 humidity variables in addition to temperature and 27 extremes indices:


Brief description of the data


Land

HadISDH utilises simultaneous subdaily temperature and dew point temperature data from >4500 quality controlled HadISD stations that have sufficiently long records. Further information on the quality control tests and HadISD can be found here. All humidity variables are calculated at hourly resolution and monthly means are created.

Monthly means are homogenised to detect and adjust for features within the data that do not appear to be of climate origin. While unlikely to be perfect, this process does help remove large errors from the data an improve robustness of long-term climate monitoring. We have used NCEI's Pairwise Homogenisation Algorithm directly on DPD and T. We have designed an indirect PHA method (ID PHA) whereby changepoints detected in DPD and T are used to make adjustments to q, e, Tw and RH. Changepoints from DPD are also applied to T. Td is derived from homogenised T and DPD. Further information can be found here. Stations with very large (>5 °C in T and Td, > 3 g kg-1 in q and > 15 %rh in RH) adjustments applied are removed.

Measurement, climatological and homogeneity adjustment uncertainty is estimated for each month.

Climatological averages over the 1991 to 2020 period are calculated and monthly mean climate anomalies obtained. These anomalies (in addition to climatological mean and standard deviation, actual values and uncertainty components) are then averaged over 5° by 5° gridboxes centred on -177.5°W and -87.5°S to 177.5°E and 87.5°N.

Given the uneven distribution of stations over time and space, sampling uncertainty is estimated for each gridbox month. Further in formation on uncertainty estimates can be found here.


Marine

HadISDH.marine utilises simultaneous subdaily air temperature and dew point temperature data from ships, moored buoys and ocean platforms from ICOADS.3.0.0 and ICOADS.3.0.2 (Freeman et al., 2016). All humidity variables are calculated at hourly resolution.

Hourly humidity and temperature values are quality controlled to to remove gross random errors (bad locations, bad timings, climatological outliers, neighbourhood outliers). Bias adjustments are also applied to the hourly data to account for increasing ship heights over time and changing proportions of poorly ventilated instruments. The data are then averaged over 5° by 5° gridboxes centred on -177.5°W and -87.5°S to 177.5°E and 87.5°N for each month as anomalies and actual values. No interpolation is applied.

Data are available as monthly mean anomaly values relative to 1991 to 2020 climatology, actual values, climatologies and a climatological standard deviation.

Uncertainty has been assessed at the observation level for measurement uncertainty, rounding uncertainty, climatology uncertainty, height adjustment uncertainty and ventilation adjustment uncertainty. These are made available at the gridbox monthly mean level along with spatio-temporal sampling uncertainty.


Blend

HadISDH.blend combines HadISDH.land and HadISDH.marine. Where both land and marine gridboxes are present a weighted average is taken based on land fraction with a lower limit of 0.25/0.75 enforced when either the land fraction is below 25% or above 75%.


Extremes

HadISDH utilises simultaneous subdaily wet bulb and dry bulb temperature from >4500 quality controlled HadISD stations that have sufficiently long records. Further information on the quality control tests and HadISD can be found here. The wet bulb temperature is calculated from the dry bulb temperature dew point temperature, with climatological surface pressure from ERA5 at hourly resolution and monthly indices are created.

Monthly indices are not homogenised. However, inhomogeneity information from the equivalent HadISDH.land monthly means is used to provide homogeneity scores for each gridbox month. These can be used to filter the data to remove gridbox months that are affected by large inhomogeneities. We recommend screening to remove gridboxes with homogeneity scores (HQ Flag) of >= 7. While unlikely to be perfect, this does help remove large errors from the data an improve robustness of long-term climate monitoring.

Climatological averages over the 1991 to 2020 period are calculated and monthly climate anomalies obtained. These anomalies (in addition to climatological mean and standard deviation and actual values) are then averaged over 5° by 5° gridboxes centred on -177.5°W and -87.5°S to 177.5°E and 87.5°N.


Keep in touch

Follow us on twitter: @metofficeHadOBS for updates, news and announcements.

For more detailed information, follow our HadISDH blog. Here we describe bug fixes, routine updates and other exploratory analysis.


2022 ANNUAL ANOMALIES (1991-2020 climatology)

HadISDH.blendq

HadISDH.blende

HadISDH.blendTd

HadISDH.blendDPD

HadISDH.blendRH

HadISDH.blendTw

HadISDH.blendT

Go to download page Data are freely available from the: land download page; marine download page; blend download page; and extremes download page. Please read the licence conditions. Data are also available from the CEDA Catalogue.

Commercial and media enquiries

You can access the Met Office Customer Centre, any time of the day or night by phone, fax or e-mail. Trained staff will help you find the information or products that are right for you.


Contact the Met Office Customer Centre


References

When using the datasets please use the following citations and state the version used:

Land

Willett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E., Jones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and temperature record for climate monitoring, Clim. Past, 10, 1983-2006, doi:10.5194/cp-10-1983-2014, 2014.
Main Text PDF file 2.6Mb
Supplementary Material PDF file 1.8Mb

Smith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704\u2013708, doi:10.1175/2011BAMS3015.1.
Available from BAMS

We strongly recommend that you read the Willett et al. (2014) paper before making use of the data. Additionaly information can be found in the reference for version 1.0.0:

Willett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface specific humidity product for climate monitoring. Climate of the Past, 9, 657-677, doi:10.5194/cp-9-657-2013.
PDF file (5.4MB)


Marine

Willett, K. M., Dunn, R. J. H., Kennedy, J. J., and Berry, D. I. 2020: Development of the HadISDH marine humidity climate monitoring dataset. Earth System Science Data. 12, 2853-2880, doi.org/10.5194/essd-12-2853-2020.
Main Paper (PDF 7.6 Mb)
Supplement (PDF 3.5 Mb)

Freeman, E., S.D. Woodruff, S.J. Worley, S.J. Lubker, E.C. Kent, W.E. Angel, D.I . Berry, P. Brohan, R. Eastman, L. Gates, W. Gloeden, Z. Ji, J. Lawrimore, N.A. Rayner, G. Rosenhagen, and S. R. Smith, 2016: ICOADS Release 3.0: A major update to the historical marine climate record. Int. J. Climatol. (doi:10.1002/joc.4775).
Available from IJC


Blend

Please use all of the above references.


Extremes

Willett, K, 2023: HadISDH.extremes Part 1: a gridded wet bulb temperature extremes index product for climate monitoring. Advances in Atmospheric Sciences, doi: 10.1007/s00376-023-2347-8. http://www.iapjournals.ac.cn/aas/en/article/doi/10.1007/s00376-023-2347-8
Part 1 (PDF x.x Mb)

Willett, K. 2023: HadISDH.extremes Part 2: exploring humid heat extremes using wet bulb temperature indices. Advances in Atmospheric Sciences, doi: 10.1007/s00376-023-2348-7. http://www.iapjournals.ac.cn/aas/en/article/doi/10.1007/s00376-023-2348-7
Part 2 (PDF x.x Mb)



Useful Diagnostics

Figures from the Willett et al. 2014 paper.

Figures from the Willett et al. 2013 paper.

Figures from State of the Climate and IPCC monitoring plots.

Decadal Trend maps for each variable: LAND; MARINE; BLEND; EXTREMES.

Annual and monthly average timeseries for the globe, hemispheres and tropics, including uncertainty estimates: LAND; MARINE; BLEND; EXTREMES.

Annual anomaly maps from 1973 onwards for specific humidity: LAND; MARINE; BLEND.

Annual anomaly maps from 1973 onwards for relative humidity: LAND; MARINE; BLEND.

Annual anomaly maps from 1973 onwards for vapour pressure: LAND; MARINE; BLEND.

Annual anomaly maps from 1973 onwards for dew point temperature: LAND; MARINE; BLEND.

Annual anomaly maps from 1973 onwards for wet bulb temperature: LAND; MARINE; BLEND.

Annual anomaly maps from 1973 onwards for air temperature: LAND; MARINE; BLEND.

Annual anomaly maps from 1973 onwards for dew point depression: LAND; MARINE; BLEND.

Extremes index annual anomaly maps from 1973 onwards for: TwX; TwX90p; TwN10p.

Our versioning system is of the form HadISDH.type.X.Y.Z.1234i. 'type' refers to the variable (e.g., landq=specific humidity). 'X' is for a major change and would be accompanied by a peer-reviewed paper or Met Office Technical Note. 'Y' is a more minor change, e.g., in one of the QC tests or homogenisation algorithms and would be described in a tech-note. 'Z' is a small change, for example addition or changes to data in the past. The last complete year of the dataset is given by '1234', and the final character shows if the dataset is f-final or p-preliminary. Therefore HadISDH.landq.2.0.0.2013p is the preliminary version of the dataset containing data up to the end of 2012.


Dataset and Diagnostic Creation Code

The Python 3 code used (excluding quality control, homogenisation and regional average uncertainty estimates) was written by Kate Willett.


Dataset produced in collaboration with:

Met Office NOAA NCEI CICS-NC CRU NPL NOC