EXPLAIN FIVE (5) CLIMATE VARIABLES AND THEIR SOURCES

BY

Ahmad Abubakar Babba SPS/21/MGE/00042

Climate Data Analysis

(NRM 8219)

Submitted to
Prof M.M. Badamasi

MSc Natural Resource Management and Climate Change

DEPARTMENT OF GEOGRAPHY
FACAULTY OF EARTH AND ENVIRONMENTAL SCIENCE
BAYERO UNIVERSITY, KANO

November 2023
1.0 Introduction

Climate variables are fundamental components of Earth's climate system, and understanding
them is crucial for comprehending the complex processes that govern our planet's climate. These
variables represent different aspects of the atmosphere, oceans, land surfaces, and ice cover, and
they interact with each other in intricate ways.

1.1.0 Climate variables

1.1.1 Temperature: Temperature is perhaps the most familiar climate variable to many people.
It refers to the degree of hotness or coldness of the atmosphere or a substance. Global
temperature patterns influence weather patterns, ecosystems, and human activities. Rising
global temperatures, attributed primarily to human-induced greenhouse gas emissions,
are driving changes in climate patterns worldwide.
1.1.2 Precipitation: Precipitation encompasses various forms of water that fall from the
atmosphere to the Earth's surface, including rain, snow, sleet, and hail. Precipitation
patterns determine the distribution of water resources, agricultural productivity, and the
occurrence of natural disasters such as floods and droughts. Changes in precipitation
patterns, such as shifts in rainfall intensity and distribution, are key indicators of climate
change impacts.
1.1.3 Atmospheric Pressure: Atmospheric pressure refers to the force exerted by the weight of
air molecules in the atmosphere. Variations in atmospheric pressure drive weather
systems and circulation patterns, influencing wind patterns, storm development, and
regional climate characteristics. Understanding atmospheric pressure gradients is
essential for weather forecasting and climate modeling.
1.1.4 Humidity: Humidity measures the amount of water vapor present in the air. It plays a
crucial role in determining atmospheric stability, cloud formation, and precipitation
processes. High humidity levels can lead to discomfort and exacerbate the impacts of
extreme heat events, while low humidity levels can increase the risk of wildfires and
droughts.
1.1.5 Wind: Wind is the movement of air relative to the Earth's surface. It is driven by
differences in atmospheric pressure and temperature gradients. Wind patterns vary
spatially and temporally, influenced by factors such as land-sea temperature contrasts,

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 topography, and global circulation patterns like the jet streams. Wind plays a vital role in
distributing heat and moisture around the globe, shaping regional climates and weather
patterns.

2.0 Major sources of climate data

2.1. WorldClim

WorldClim is a set of global climate layers (gridded climate data) with a spatial resolution of
about 1 km2 in Version 1 and 2, and lower spatial resolutions in Version 2. The ANUSPLIN
software is used to interpolate climate variables observed at weather stations for both versions.
ANUSPLIN is a program for interpolating noisy multi-variate data using thin plate smoothing
splines. Latitude, longitude, and elevation were used as independent variables.

2.1.1 WorldClim Version 1

WorldClim version 1 has average monthly climate data for minimum, mean, and maximum
temperature and for precipitation for 1960-1990. The data layers were generated through
interpolation of average monthly climate data from weather stations. Variables included are
monthly total precipitation, and monthly mean, minimum and maximum temperature, and 19
derived bioclimatic variables (https://www.worldclim.org/bioclim).

Available climate data for free download (https://www.worldclim.org/version1) include:

 Current conditions (interpolations of observed data, representative of 1960-1990)

 Future conditions: downscaled global climate model (GCM) data from CMIP5 (IPPC
Fifth Assessment)
 Past conditions (downscaled global climate model output)

2.1.2 WorldClim Version2

WorldClim version 2 (https://www.worldclim.org/version2) has average monthly climate data for

minimum, mean, and maximum temperature and for precipitation for 1970-2000 only for now.
Climate variables can be downloaded for different spatial resolutions, from 30 seconds (~1 km2)

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to 10 minutes (~340 km2). More climate variables are included in this version, including solar
radiation, wind speed, and water vapour.

Figure 1: Global mean annual temperature (°C). Images by Tongli Wang under CC BY-SA

2.2. CRU climate data

The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.03 data are month-
by-month variations in climate over the period 1901-2018, provided on high-resolution (0.5×0.5
degree) grids, produced by CRU at the University of East Anglia.

The CRU TS4.03 variables are cloud cover, diurnal temperature range, frost day frequency,
potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily
maximum and minimum temperature, and vapour pressure for the period January 1901 –
December 2018.

The monthly dataset was generated using Angular Distance Weighting (ADW) interpolation
approach, which weights the data points by distance relative to a correlation decay distance and
gives more weight to isolated data points. The time-series dataset can be downloaded at
https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.03/

The 0.5° x 0.5° spatial resolution is too low for ecological modeling at a local, especially in
mountainous regions.

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Figure 2: The coverage of the CRU time-series climate data and the patterns of temperatures.
https://climatedataguide.ucar.edu/climate-data/cru-ts-gridded-precipitation-and-other-
meteorological-variables-1901

2.3 PRISM climate data

The PRISM Climate Group gathers climate observations from a wide range of monitoring
networks, applies sophisticated quality control measures, and develops spatial climate datasets to
reveal short- and long-term climate patterns. The resulting datasets incorporate a variety of
modeling techniques and are available at multiple spatial/temporal resolutions, covering the

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period from 1895 to the present. It offers these datasets to the public, either free of charge or for
a fee (depending on dataset size/complexity and funding available for the activity).

The PRISM data were developed using the PRISM model that incorporates weather station data,
a digital elevation model, and expert knowledge of climate patterns such as rain shadows, coastal
effects, orographic lift, and temperature inversions over topographically delineated ‘‘facets’’
(Daly et al. 2002). PRISM data have clear advantages over other products in reflecting these
effects, especially for precipitation.

The PRISM climate datasets are available for the United States except for AK. The datasets
include PRISM normals cover the period 1981-2010, monthly for Historical Past (1895-1980)
and for Recent Years (Jan 1981 – May 2019) at http://www.prism.oregonstate.edu/.

2.4 ClimateNA

ClimateNA is a standalone MS Windows software application (Wang et al. 2016) that extracts
and downscales gridded (4 x 4 km) monthly climate data for the reference normal period (1961-
1990) from PRISM (Daly et al. 2008) and WorldClim (Hijmans et al. 2005) to scale-free point
locations. It also calculates many (>200) monthly, seasonal and annual climate variables. The
downscaling is achieved through a combination of bilinear interpolation and dynamic local
elevational adjustment. ClimateNA also uses the scale-free data as a baseline to downscale
historical and future climate variables for individual years and periods between 1901 and 2100. A
time-series function is available to generate climate variables for multiple locations and multiple
years.

The program can read and output comma-delimitated spreadsheet (CSV) files. The new version
(v6.00) can also directly read digital elevation model (DEM) raster (ASC) files and output
climate variables in raster format for mapping. The spatial resolution of the raster files is up to
the user’s preference. The coverage of the program is shown in Figure 3.

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Figure 3: The coverage of ClimateNA. The extent of the coverage: longitude = -179.167 ~ -
52.625°; latitude = 14.453 ~ 83.203°. Image by Tongli Wang under CC BY-SA.

2.4 Other Sources of Geospatial Data and Products

In this subsection, we will discuss about different sources of geospatial data which you can
acquire either on cost or for free. We will also briefly discuss about how to find data and the kind
of data available at different sources. With the evolution of various sensors such as LANDSAT
from USA, IRS i.e. Indian Remote Sensors series and Indian geostationary Satellite series i.e.
INSAT from India, ENVISAT from European Space Agency, SCHYMACHY from USA, TRMM
and Terra/Aqua-MODIS from USA/NASA etc., data have been acquired and used as the primary
data source for earth’s resources and atmospheric monitoring since 1972. There are numerous
missions dedicated to the study of specific types of land, ocean or atmospheric parameters.
Importance of geoinformatics for retrieving variables related to climate change studies is very
important. Recently a special issue was devoted to remote sensing of ECVs, which can be
accessed at www.mdpi.com/journal/remotesensing/special_issues/Climate_Variables.

In India, user can acquire remote sensing data from National Data Centre (NDC) of National
Remote Sensing Centre (NRSC), of ISRO, which is located at Hyderabad. A variety of remote
sensing data and several derived products from many satellitesare available at the following
portals:

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  http://glcf.umiacs.umd.edu/data
 https://earthexplorer.usgs.gov/
 https://neo.sci.gsfc.nasa.gov/
 https://glovis.usgs.gov
 https://scihub.copernicus.eu/dhus/
 https://lpdaac.usgs.gov/news/?&page=13
 https://search.earthdata.nasa.gov/
 www.vito-eodata.be/
 www.class.ngdc.noaa.gov/saa/products/welcome
 https://coast.noaa.gov/digitalcoast/
 www.terrapop.org/
 https://earthdata.nasa.gov/earth-observation-data/near-real-time/rapidresponse
 www.vito-eodata.be/PDF/portal/Application.html#Home
 www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
 https://eospso.gsfc.nasa.gov/content/nasa-earth-science-data
 https://clim-engine-development.appspot.com/fewsNet

OTHER SOURCES

Atmospheric Precipitation www.esrl.noaa.gov/psd/data/gridded/

data.cmap.html#detail
http://precip.gsfc.nasa.gov/
http://vlb.ncdc.noaa.gov/temp-and-precip/msu
https://earlywarning.usgs.gov/fews/datadownloads/
Global/CHIRPS%202.0
www.ncdc.noaa.gov/temp-and-precip/global-maps/
www.ssd.noaa.gov/PS/TROP/etrap.html
www.ospo.noaa.gov/Products/atmosphere/mspps/
rainprd.html
Temperature http://vlb.ncdc.noaa.gov/temp-and-precip/msu
www.ncdc.noaa.gov/temp-and-precip/msu/
www.star.nesdis.noaa.gov/smcd/emb/mscat/
index.php
www.ospo.noaa.gov/Products/atmosphere/
soundings/atovs/profiles/index.html
www.ospo.noaa.gov/Products/atmosphere/
soundings/iasi

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 Water vapour http://disc.sci.gsfc.nasa.gov/data-holdings/PIP/
atmospheric_water_vapor_or_humidity.shtml
https://eosweb.larc.nasa.gov/project/nvap/nvapm_
table
Wind speed http://podaac.jpl.nasa.govdatasetlist?search=CCMP
http://manati.star.nesdis.noaa.gov/index.php
and direction http://mscweb.kishou.go.jp/product/product/amv/
index.htm
http://navigator.eumetsat.int/discovery/Start/
Explore/Quick.do
www.goes.noaa.gov/WINDS/index.html
www.ssd.noaa.gov/PS/WIND/
www.ospo.noaa.gov/Products/atmosphere/winds/
winds_month.html
http://manati.orbit.nesdis.noaa.gov/datasets/ASCATData.php
www.ospo.noaa.gov/Products/land/spp/
sharedprocessing.html#WS
Radiation https://eosweb.larc.nasa.gov
budget http://isccp.giss.nasa.gov/projects/flux.
htmhttp://disc.sci.gsfc.nasa.gov/AIRS/data-holdings/
by-access-method
www.ospo.noaa.gov/Products/atmosphere/clavr/index.html
www.ospo.noaa.gov/Products/atmosphere/rad_budget.html#ABS
www.ospo.noaa.gov/Products/atmosphere/rad_budget.html#AVL

REFERENCES

Bojinski, S., Verstraete, M., Peterson, T.C., Richter, C., Simmons, A., and Zemp, M. (2014). The
concept of essential climate variables in support of climate research, applications, and policy,
[https://journals.ametsoc.org/doi/pdf/10.1175/BAMS-D13-00047.1]

Wang T (2021) Climatic & Ecological Modelling for Adaptive Forest Applications. B Campus.
https://pressbooks.bccampus.ca/climatemodellingforestadaptation/chapter/topic-2-1-
introduction-to-climate-models/

Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., Xu, B., Shi, J., and Dickinson, R.
(2013) The role of satellite remote sensing in climate change studies, Nature Climate Change,
DOI: 10.1038/NCLIMATE1908 www.eenews.net/ assets/2014/02/11/document_cw_01.pdf.

APPENDIX

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