ClimateNA Map Version


Tongli Wang

Centre for Forest Conservation Genetics (CFCG)

Department of Forest Science, University of British Columbia


May 08, 2022



About this program.. 1

Features. 3

How to use. 3

Data sources. 3

Baseline data. 3

Historical data. 3

Future climate data. 3

Climate variables. 4

1) Annual variables: 4

2) Seasonal variables: 4

3) Monthly variables. 6

How to refer. 7

Acknowledgements. 7


About this program


ClimateNA_Map is a Google Maps based version of ClimateNA v7.30. Users can simply click on the map to get climate data for the locations of your interest. The program extracts and downscales 1961-1990 monthly climate normal data from a moderate spatial resolution (4 x 4 km) to scale-free point locations, and calculates many (>200) monthly, seasonal and annual climate variables for specific locations based on latitude, longitude and elevation. The downscaling is achieved through a combination of bilinear interpolation and dynamic local elevational adjustment. ClimateNA also uses the scale-free data as baseline to downscale historical and future climate variables for individual years and periods between 1901 and 2100. For predictions of multiple locations and for more GCMs, we recommend you to download the standalone version at http://climatena.ca/.



1.      Obtain climate data by clicking on the map. The values of latitude, longitude and elevation are generated by Google Maps geo-positioning system and Google Elevation Service. Alternatively, latitude, longitude and elevation can also be input manually.

2.      Changed periods (historical and future) using the dropdown menus.

3.      Visualize spatial climate layers overlaid over Google Maps.

4.      Save outputs for multiple locations/periods to a file on your local computer.


Data sources

Baseline data

The monthly baseline data for 1961-1990 normals were compiled from the following sources and unified at 4 x 4 km spatial resolution:

1. British Columbia: PRISM at 800 x 800 m from Pacific Climate Impact Consortium;

2. Parries provinces: PRISM at 4 x 4 km from the PRISM Climate Group (http://www.prism.oregonstate.edu/);

3. United States: PRISM at 800 x 800 m from the PRISM Climate Group (Daly et al. 2008);

4. The rest: ANUSPLIN at 4 x 4 km

5. The monthly solar radiation data were provided by Dr. Robbie Hember at University of British Columbia.


Historical data

Historical monthly data for the years 1901- 2020 were gerated on our own. Reference is not available yet.


Future climate data

Future climate projections were selected from the General Circulation Models (GCMs) of the Coupled Model Intercomparison Project (CMIP6) to be included in IPCC sixth assessment report (AR6). The new set of emissions scenarios from CMIP6, called Shared Socioeconomic Pathways (SSPs), included SSP126, SSP245, SSP460, SSP370 and SSP585. We included the ensembles among the 13 GCMs.


Climate variables

1) Annual variables:

Directly calculated annual variables:

MAT mean annual temperature (C),

MWMT mean warmest month temperature (C),

MCMT mean coldest month temperature (C),

TD temperature difference between MWMT and MCMT, or continentality (C),

MAP mean annual precipitation (mm),

MSP May to September precipitation (mm),

AHM annual heat-moisture index (MAT+10)/(MAP/1000))

SHM summer heat-moisture index ((MWMT)/(MSP/1000))

Derived annual variables:

DD<0 degree-days below 0C, chilling degree-days

DD>5 degree-days above 5C, growing degree-days

DD<18 degree-days below 18C, heating degree-days

DD>18 degree-days above 18C, cooling degree-days

NFFD the number of frost-free days

FFP frost-free period

bFFP the day of the year on which FFP begins

eFFP the day of the year on which FFP ends

PAS precipitation as snow (mm) between August in previous year and July in current year

EMT extreme minimum temperature over 30 years

EXT extreme maximum temperature over 30 years

Eref Hargreaves reference evaporation (mm)

CMD Hargreaves climatic moisture deficit (mm)

MAR mean annual solar radiation (MJ m2 d1)

RH mean annual relative humidity (%)

CMI Hoggs climate moisture index (mm)

DD1040 (10<DD<40) degree-days above 10C and below 40C


2) Seasonal variables:


Winter (_wt): Dec. (prev. yr) - Feb for annual, Jan, Feb, Dec for normals

Spring (_sp): March, April and May

Summer (_sm): June, July and August

Autumn (_at): September, October and November


Directly calculated seasonal variables:

Tave_wt winter mean temperature (C)

Tave_sp spring mean temperature (C)

Tave_sm summer mean temperature (C)

Tave_at autumn mean temperature (C)


Tmax_wt winter mean maximum temperature (C)

Tmax_sp spring mean maximum temperature (C)

Tmax_sm summer mean maximum temperature (C)

Tmax_at autumn mean maximum temperature (C)


Tmin_wt winter mean minimum temperature (C)

Tmin_sp spring mean minimum temperature (C)

Tmin_sm summer mean minimum temperature (C)

Tmin_at autumn mean minimum temperature (C)


PPT_wt winter precipitation (mm)

PPT_sp spring precipitation (mm)

PPT_sm summer precipitation (mm)

PPT_at autumn precipitation (mm)


RAD_wt winter solar radiation (MJ m2 d1)

RAD_sp spring solar radiation (MJ m2 d1)

RAD_sm summer solar radiation (MJ m2 d1)

RAD_at autumn solar radiation (MJ m2 d1)


Derived seasonal variables:

DD_0_wt winter degree-days below 0C

DD_0_sp spring degree-days below 0C

DD_0_sm summer degree-days below 0C

DD_0_at autumn degree-days below 0C


DD5_wt winter degree-days below 5C

DD5_sp spring degree-days above 5C

DD5_sm summer degree-days above 5C

DD5_at autumn degree-days above 5C


DD_18_wt winter degree-days below 18C

DD_18_sp spring degree-days below 18C

DD_18_sm summer degree-days below 18C

DD_18_at autumn degree-days below 18C


DD18_wt winter degree-days below 18C

DD18_sp spring degree-days above 18C

DD18_sm summer degree-days above 18C

DD18_at autumn degree-days above 18C


NFFD_wt winter number of frost-free days

NFFD_sp spring number of frost-free days

NFFD_sm summer number of frost-free days

NFFD_at autumn number of frost-free days


PAS_wt winter precipitation as snow (mm)

PAS_sp spring precipitation as snow (mm)

PAS_sm summer precipitation as snow (mm)

PAS_at autumn precipitation as snow (mm)


Eref_wt winter Hargreaves reference evaporation (mm)

Eref_sp spring Hargreaves reference evaporation (mm)

Eref_sm summer Hargreaves reference evaporation (mm)

Eref_at autumn Hargreaves reference evaporation (mm)


CMD_wt winter Hargreaves climatic moisture deficit (mm)

CMD_sp spring Hargreaves climatic moisture deficit (mm)

CMD_sm summer Hargreaves climatic moisture deficit (mm)

CMD_at autumn Hargreaves climatic moisture deficit (mm)


RH_wt winter relative humidity (%)

RH_sp winter relative humidity (%)

RH_sm winter relative humidity (%)

RH_at winter relative humidity (%)


CMI_wt winter Hoggs climate moisture index (mm)

CMI_sp spring Hoggs climate moisture index (mm)

CMI_sm summer Hoggs climate moisture index (mm)

CMI_at autumn Hoggs climate moisture index (mm)



3) Monthly variables

Primary monthly variables:

Tave01 Tave12 January - December mean temperatures (C)

TMX01 TMX12 January - December maximum mean temperatures (C)

TMN01 TMN12 January - December minimum mean temperatures (C)

PPT01 PPT12 January - December precipitation (mm)

RAD01 RAD12 January - December solar radiation (MJ m2 d1)


Derived monthly variables:

DD_0_01 DD_0_12 January - December degree-days below 0C

DD5_01 DD5_12 January - December degree-days above 5C

DD_18_01 DD_18_12 January - December degree-days below 18C

DD18_01 DD18_12 January - December degree-days above 18C

NFFD01 NFFD12 January - December number of frost-free days

PAS01 PAS12 January December precipitation as snow (mm)

Eref01 Eref12 January December Hargreaves reference evaporation (mm)

CMD01 CMD12 January December Hargreaves climatic moisture deficit (mm)

RH01 RH12 January December relative humidity (%)

CMI01 CMI12 January December Hoggs climate moisture index (mm)


How to refer


Wang T, Hamann A, Spittlehouse D, Carroll C, 2016. Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. doi:10.1371/journal.pone.0156720

Mahony CR, Wang T; Hamann A and Cannon AJ, 2021. A CMIP6 ensemble for downscaled monthly climate normals over North America. EarthArXiv. https://doi.org/10.31223/X5CK6Z




Funding for this study was provided by the Forest Genetic Council of BC (FGC) and Ministry of Forests, Lands and Natural Resource Operations.