This statistic shows the average snowfall for Canada from 1971 to 2000, by city. Victoria, Canada averaged 43.8 centimeters of snowfall annually from 1971 to 2000.

Description (in English, French follows)

The Canadian historical Snow Water Equivalent dataset (CanSWE) includes manual and automated pan-Canadian observations of Snow Water Equivalent (SWE) collected by national, provincial and territorial agencies, hydropower companies and their partners, as well as academic institutions. Snow depth and derived bulk snow density are also included when available. A code describes the SWE measurement method for each site following World Meteorological Organization (WMO) standards (WMO, 2019). This new dataset supersedes the most recent update of the Canadian Historical Snow Survey (CHSSD) dataset published by Brown et al. (2019) and available at https://doi.org/10.18164/cf337b6b-9a87-4ffd-a8e5-41e6498b1474. The creation of CanSWE used the 2019 CHSSD update as a starting point and involved three main steps: (i) correction and cleaning of the 2019 CHSSD update (correction of metadata, removal of duplicates), (ii) update of this cleaned dataset until July 2020 and addition of snow data from new stations and agencies, and (iii) consistent quality control of the final dataset. The version 6 of CanSWE includes over one million SWE measurements from 2921 different locations across Canada over the snow seasons 1928 – 2023 where a snow season is defined as starting August 01 and ending July 31. CanSWE is described in detail in Vionnet et al. (2021).

The data are distributed in 2 formats: a NetCDF file (CanSWE-CanEEN_1928-2023_v6.nc) and a zip file containing a csv version of CanSWE (CanSWE-CanEEN_1928-2023_v6.zip). More details about the dataset, the file format and the update made in CanSWEv6 are given in the files ReadMe_CanSWE_v6.pdf (in English) and LisezMoi_CanEEN_v6.pdf (in French).

Description (Francais)

La base données historiques canadiennes d’Equivalent en Eau de la Neige (CanEEN) comprend des observations manuelles et automatiques de l’Equivalent en Eau de la Neige (EEN) à l’échelle du Canada collectées par des agences nationales, provinciales et territoriales, des compagnies productrices d’hydroélectricité et leurs partenaires ainsi que par des universités. Les informations sur la hauteur de neige et la masse volumique moyenne du manteau neigeux sont incluses lorsqu’elles sont disponibles. Un code qui suit les règles de l’Organisation Mondiale de la Météorologie (OMM, 2019) décrit la méthode de mesure de l’EEN pour chaque site. Cette nouvelle base de données remplace le jeu de données des Relevés Nivométriques Canadiens (RNC) publié par Brown et al. (2019) et disponible à l’adresse : https://doi.org/10.18164/cf337b6b-9a87-4ffd-a8e5-41e6498b1474. La création de CanEEN se base sur la version de 2019 des RNC et se décompose en 3 étapes principales : (i) correction et nettoyage de la version 2019 des RNC (correction des métadonnées, suppression des duplicata), (ii) mise à jour de ce jeu de données nettoyé avec des données disponibles jusqu’en Juillet 2020 et ajout de données historiques provenant de nouvelles stations et de nouveaux partenaires, (iii) contrôle qualité appliqué à l’ensemble du jeu de données. La version 6 de CanEEN inclut plus d’un million de mesures de l’EEN collectées dans 2921 stations à travers le Canada pour les années nivologiques 1928 à 2023 où une année nivologique est définie pour la période allant du 1 août au 31 juillet. CanEEN est décrit en détail dans Vionnet et al. (2021).

Les données sont distribuées sous deux formats: un fichier au format NetCDF (CanSWE-CanEEN_1928-2023_v6.nc) et une archive zip contenant un fichier au format csv (CanSWE-CanEEN_1928-2023_v6.csv). Des informations complémentaires sur le jeu de données, leur format ainsi que les modifications apportées dans la version 6 sont fournies dans les fichiers ReadMe_CanSWE_v6.pdf (en Anglais) et LisezMoi_CanEEN_v6.pdf (en Francais).

References/Références:

Brown, R. D., Fang, B., and Mudryk, L.: Update of Canadian historical snow survey data and analysis of snow water equivalent trends, 1967–2016. Atmos. Ocean, 57, 149 156, https://doi.org/10.1080/07055900.2019.1598843, 2019

Vionnet, V., Mortimer, C., Brady, M., Arnal, L., and Brown, R.: Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020), Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, 2021.

WMO (World Meteorological Organization): Global Cryosphere Watch: Improvements in the international reporting of Snow Depth, WIGOS Newsletter, 5, 3-4, https://community.wmo.int/wigos-newsletters-archive, 2019

These daily weather records were compiled from a subset of stations in the Global Historical Climatological Network (GHCN)-Daily dataset. A weather record is considered broken if the value exceeds the maximum (or minimum) value recorded for an eligible station. A weather record is considered tied if the value is the same as the maximum (or minimum) value recorded for an eligible station. Daily weather parameters include Highest Min/Max Temperature, Lowest Min/Max Temperature, Highest Precipitation, Highest Snowfall and Highest Snow Depth. All stations meet defined eligibility criteria. For this application, a station is defined as the complete daily weather records at a particular location, having a unique identifier in the GHCN-Daily dataset. For a station to be considered for any weather parameter, it must have a minimum of 30 years of data with more than 182 days complete in each year. This is effectively a 30-year record of service requirement, but allows for inclusion of some stations which routinely shut down during certain seasons. Small station moves, such as a move from one property to an adjacent property, may occur within a station history. However, larger moves, such as a station moving from downtown to the city airport, generally result in the commissioning of a new station identifier. This tool treats each of these histories as a different station. In this way, it does not thread the separate histories into one record for a city. Records Timescales are characterized in three ways. In order of increasing noteworthiness, they are Daily Records, Monthly Records and All Time Records. For a given station, Daily Records refers to the specific calendar day: (e.g., the value recorded on March 7th compared to every other March 7th). Monthly Records exceed all values observed within the specified month (e.g., the value recorded on March 7th compared to all values recorded in every March). All-Time Records exceed the record of all observations, for any date, in a station's period of record. The Date Range and Location features are used to define the time and location ranges which are of interest to the user. For example, selecting a date range of March 1, 2012 through March 15, 2012 will return a list of records broken or tied on those 15 days. The Location Category and Country menus allow the user to define the geographic extent of the records of interest. For example, selecting Oklahoma will narrow the returned list of records to those that occurred in the state of Oklahoma, USA. The number of records broken for several recent periods is summarized in the table and updated daily. Due to late-arriving data, the number of recent records is likely underrepresented in all categories, but the ratio of records (warm to cold, for example) should be a fairly strong estimate of a final outcome. There are many more precipitation stations than temperature stations, so the raw number of precipitation records will likely exceed the number of temperature records in most climatic situations.

As of October 2024, the annual snowfall depth in Aomori amounted to 460 centimeters. Figures hit a decade-low in 2020, reaching only 264 centimeters of snowfall depths. Aomori is the capital city of Aomori Prefecture, which is located in the Tohoku region of Japan. It is one of the Japanese cities with the heaviest snowfall.

As of October 2024, there were ten centimeters of annual snowfall recorded in Japan's capital city Tokyo. Figures were highest in 2018, reaching 24 centimeters of annual snowfall depth. Tokyo lies in the humid subtropical climate zone and has mild to cool winters.

These daily weather records were compiled from a subset of stations in the Global Historical Climatological Network (GHCN)-Daily dataset. A weather record is considered broken if the value exceeds the maximum (or minimum) value recorded for an eligible station. A weather record is considered tied if the value is the same as the maximum (or minimum) value recorded for an eligible station. Daily weather parameters include Highest Min/Max Temperature, Lowest Min/Max Temperature, Highest Precipitation, Highest Snowfall and Highest Snow Depth. All stations meet defined eligibility criteria. For this application, a station is defined as the complete daily weather records at a particular location, having a unique identifier in the GHCN-Daily dataset. For a station to be considered for any weather parameter, it must have a minimum of 30 years of data with more than 182 days complete in each year. This is effectively a 30-year record of service requirement, but allows for inclusion of some stations which routinely shut down during certain seasons. Small station moves, such as a move from one property to an adjacent property, may occur within a station history. However, larger moves, such as a station moving from downtown to the city airport, generally result in the commissioning of a new station identifier. This tool treats each of these histories as a different station. In this way, it does not thread the separate histories into one record for a city. Records Timescales are characterized in three ways. In order of increasing noteworthiness, they are Daily Records, Monthly Records and All Time Records. For a given station, Daily Records refers to the specific calendar day: (e.g., the value recorded on March 7th compared to every other March 7th). Monthly Records exceed all values observed within the specified month (e.g., the value recorded on March 7th compared to all values recorded in every March). All-Time Records exceed the record of all observations, for any date, in a station's period of record. The Date Range and Location features are used to define the time and location ranges which are of interest to the user. For example, selecting a date range of March 1, 2012 through March 15, 2012 will return a list of records broken or tied on those 15 days. The Location Category and Country menus allow the user to define the geographic extent of the records of interest. For example, selecting Oklahoma will narrow the returned list of records to those that occurred in the state of Oklahoma, USA. The number of records broken for several recent periods is summarized in the table and updated daily. Due to late-arriving data, the number of recent records is likely underrepresented in all categories, but the ratio of records (warm to cold, for example) should be a fairly strong estimate of a final outcome. There are many more precipitation stations than temperature stations, so the raw number of precipitation records will likely exceed the number of temperature records in most climatic situations.

Anomalous weather resulting in Temperature and Precipitation extremes occurs almost every day somewhere in Canada. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. Virtual Climate stations correspond with the city pages of weather.gc.ca. This data provides the daily extremes of record for Snowfall for each day of the year. Daily elements include: Greatest Snowfall.

As of October 2024, the annual snowfall depth in Sapporo, Hokkaido, amounted to 459 centimeters. Sapporo is the capital city of Hokkaido, the northernmost prefecture of Japan. Because of its cold and temperate climate, it is a popular destination for winter sports.

Time series of daily Snow Water Equivalent (SWE) and Snow Density over the Northern Hemisphere, based on in-situ station observations of snow depth converted to SWE using the ΔSNOW model (Winkler et al., 2021) and regionalised parameters.

An extensive description of the dataset and the method to generate it can be found in the data descriptor manuscript published in the journal Earth System Science Data: https://essd.copernicus.org/articles/15/2577/2023/essd-15-2577-2023

Dataset: A total of 11,0071 time series of modelled SWE and estimated snow density at the point scale, spanning 1950-2022, at daily resolution. "NH-SWE_dataset_MAP.png" shows a Northern Hemisphere map with the location of all stations in the NH-SWE dataset and their elevation in meters.

Files: The dataset is provided in two different formats:

1. Individual .csv files for each station in the NH-SWE dataset at "NH_SWE_dataset_vector_files.zip"
2. Full-dataset .csv matrices with dates as rows and NH-SWE stations as columns at "NH_SWE_dataset_matrix_files.zip"

Metadata: "NH_SWE_METADATA.csv" Includes information on NH-SWE stations location (ID, country, station name, coordinates, elevation), data source, length of time series, model parameters and the climate variables used to estimate them, and average snow climatology such as average maximum snow depth, average peak SWE and average maximum snow cover duration. More details and units in the "README_fileformats.txt" file.

ΔSNOW model parameter regionalisation: The code to obtain the ΔSNOW model parameters based on climate variables for all the stations in the NH-SWE dataset is shared in "DeltaSNOW_parameter_regionalisation.zip". The method is extensively described in the data descriptor manuscript by Fontrodona-Bach et al., (2023) submitted to Earth System Science Data. More details in the "README_regionalisation.txt" file.

Data use: Free, provided adequate citation of both the data descriptor manuscript and the zenodo record. See "README_datausage.txt"

Version history:
v1: Initial upload. The ΔSNOW model regionalisation was missing.
v2: Manuscript submission version. Updated dataset and includes the ΔSNOW model regionalisation code.

Reported errors:
The dataset accidentally contains one station from the Southern Hemisphere (NH-SWE ID 500001), located in Antarctica (Country code AY).
The longitude of a few stations exceeds +180 decimal degrees. To obtain the correct value within the [-180,180] decimal degree longitude bounds, the value exceeding +180 needs to be added to -180 degrees (e.g. +181.0 degrees is actually -179.0 degrees).
Swedish stations have two different country codes, SE for the ECA&D stations, and SW for the GHCNd stations.
Japan country code is "JA" in the metadata, although the official country code should be JP.

Background
Despite the infrequency of storms, the City of Colorado Springs Streets Division must be prepared for minor and major snowstorms from September through May. The City's Streets Division is responsible for servicing over 7,431 lane miles of roadway, extending over a 194 square mile area. While average annual snowfall stands at 42 inches, snow can pile up quickly, at varying rates throughout the City. Elevation and wind can compound accumulation, causing an immediate impact on the City's mobility. Hence, the need for safe and passable streets is a priority for the Streets Division.

Route Type
Primary Routes, which are multi-lane roads with large volumes of traffic or hospital access, are treated first. Once the primary routes are passable, snow crews move on to Secondary Routes, which includes school access and collector streets that serve as the main connections between neighborhoods and primary roads. If there is continuous snowfall, the Primary Routes may have to be plowed more than once, which will delay the response on secondary streets.

Street Treatments
The City uses two materials on City streets. The first material is called "anti-skid" and is used on most snow routes. Anti-skid can contain up to 20 percent salt, and is used to aid in vehicle traction. The second material is used on many of the City's thoroughfares and is called "IceSlicer." This material is a de-icer and will lower the freezing temperature of water. IceSlicer will work down to around 10 degrees.

For more information about the City of Colorado Springs Street Division, please click here

State and County Roads
All State and County roads are maintained by their own staff. For information on snow removal on State or County roads, call:

• CDOT winter road conditions, (303) 639-1111
• CDOT road maintenance office (Colorado Springs), (719) 634-2323 or (719) 576-1868
• El Paso County Department of Transportation office, (719) 520-6460

Contact information

• City of Colorado Springs Streets Division, (719) 385-5934
• 24-Hour Answering Service, (719) 278-8352
• Streets Division automated snow hotline, (719) 385-SNOW
• National Weather Service Forecast information for Colorado Springs, (303) 573-6846
• What to Know for Snow City information - WinterStorms

Snow and weather data Saskatoon, SK, Canada. Winter 2013-2014, 2014-2015Daily values for snowfall, snow depth, and snow density from citizen science study in Saskatoon, SK. Complemented with daily temperature and wind speed values.DailySnowWeatherData.txt

Background Despite the infrequency of storms, the City of Colorado Springs Streets Division must be prepared for minor and major snowstorms from September through May. The City's Streets Division is responsible for servicing over 7,431 lane miles of roadway, extending over a 194 square mile area. While average annual snowfall stands at 42 inches, snow can pile up quickly, at varying rates throughout the City. Elevation and wind can compound accumulation, causing an immediate impact on the City's mobility. Hence, the need for safe and passable streets is a priority for the Streets Division.Route TypePrimary Routes, which are multi-lane roads with large volumes of traffic or hospital access, are treated first. Once the primary routes are passable, snow crews move on to Secondary Routes, which includes school access and collector streets that serve as the main connections between neighborhoods and primary roads. If there is continuous snowfall, the Primary Routes may have to be plowed more than once, which will delay the response on secondary streets.Street Treatments The City uses two materials on City streets. The first material is called "anti-skid" and is used on most snow routes. Anti-skid can contain up to 20 percent salt, and is used to aid in vehicle traction. The second material is used on many of the City's thoroughfares and is called "IceSlicer." This material is a de-icer and will lower the freezing temperature of water. IceSlicer will work down to around 10 degrees. For more information about the City of Colorado Springs Street Division, please click hereState and County Roads All State and County roads are maintained by their own staff. For information on snow removal on State or County roads, call: CDOT winter road conditions, (303) 639-1111 CDOT road maintenance office (Colorado Springs), (719) 634-2323 or (719) 576-1868 El Paso County Department of Transportation office, (719) 520-6460 Contact information City of Colorado Springs Streets Division, (719) 385-5934 24-Hour Answering Service, (719) 278-8352 Streets Division automated snow hotline, (719) 385-SNOW National Weather Service Forecast information for Colorado Springs, (303) 573-6846 What to Know for Snow City information - WinterStorms

EngRAD Dataset

The EngRAD dataset contains measurements of 5 different weather variables collected at 487 grid points in England from 2018 to 2020.

Dataset Overview

Data has been provided by Open-Meteo and licensed under Attribution 4.0 International (CC BY 4.0). The numerical weather prediction model used to generate the data is ECMWF IFS, which has a spatial resolution of 9 km. The grid points are located in correspondence with cities. Each point is associated with location information such as geographic coordinates, elevation, closest city, and the county to which it belongs. The physical variables collected are:

1. Air temperature at 2 meters above ground (°C)
2. Relative humidity at 2 meters above ground (%)
3. Summation of total precipitation (rain, showers, snow) during the preceding hour (mm)
4. Total cloud cover (%)
5. Global horizontal irradiance (W/m²)

These variables are typically of interest in applications related to solar radiation, such as solar power production.

Channels:

The data.h5 file contains two tables, accessible by the following keys:

• data: Contains the measurements for each point across different weather variables.

  • temperature_2m: Air temperature at 2 meters above ground (°C). Instantaneous measurement.
  • relative_humidity_2m: Relative humidity at 2 meters above ground (%). Instantaneous measurement.
  • precipitation: Total precipitation (rain, showers, snow) sum of the preceding hour (mm). Preceding hour sum.
  • cloud_cover: Total cloud cover as an area fraction (%). Instantaneous measurement.
  • shortwave_radiation: Global horizontal irradiation (GHI) (W/m²). Preceding hour mean.

• metadata: Contains the following detailed information for each point:

  • city: The name of the city where the measurement point is located.
  • county: The county in which the city is situated.
  • admin_name: The administrative name associated with the city or region.
  • lat: The latitude coordinate of the measurement point.
  • lon: The longitude coordinate of the measurement point.
  • elevation: The elevation (in meters) above sea level at the measurement point.
  • population: The population of the city where the measurement point is located.

Dataset size

• Time steps: 26304
• Points: 487
• Channels: 5
• Sampling rate: 1 hour
• Missing values: 0.00%

Credits

This dataset has been introduced in the paper:

Ivan Marisca, Cesare Alippi, and Filippo Maria Bianchi. "Graph-based forecasting with missing data through spatiotemporal downsampling." Proceedings of the 41st International Conference on Machine Learning, PMLR 235:34846-34865, 2024.

Please consider citing the paper if you use the dataset for your research.

@inproceedings{marisca2024graph,
 title   = {Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling},
 author  = {Marisca, Ivan and Alippi, Cesare and Bianchi, Filippo Maria},
 booktitle = {Proceedings of the 41st International Conference on Machine Learning},
 pages   = {34846--34865},
 year   = {2024},
 volume  = {235},
 series  = {Proceedings of Machine Learning Research},
 publisher = {PMLR}
}

The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 with snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight. There is also an upload with simulation output for the same night, but without snow cover (Rauchöcker et al., 2024a). Also available in a different dataset are data from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period.

WRF Simulation Output

This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020.

Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the two simulation with snow cover, where jan126_sms.zip contains the files relating to the simulations with the SMS-3DTKE scheme and jan16.zip those for the simulation with the MYNN 2.5-order PBL parameterization. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the files namelist.input and namelist_sms.input that were used for the simulations.

Standard WRF output can be found in wrfout_40m_jan16 and wrfout_40m_jan16_sms. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in windout_40m_jan16 and windout_40m_jan16_sms. These variables were contained in the unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in tend_40m_jan16.nc and tend_40m_jan16_sms.nc.

Plotting routines

Python scripts and environment files to reproduce most figures in Rauchoecker et al. (2024d) are included in code.zip. To reproduce plots involving measurement data, which is available in Rauchöcker et al. (2024c), is also needed.

Due to conflicts between some packages, two different environment were needed. To reproduce Figure 2, install the orthoplot environment by running "conda env create orthoplot.yml" in a terminal window, activate it ("conda activate orthoplot") and then run ortho_plot.py. All other plots require the wrfstuff environent (installed by running "conda env create wrfstuff.yml" and activated by "conda activate wrfstuff") and are produced by paper_plots.py. Functions used to load data and plot the figures are included in dataload.py and plotting_routines.py, respectively.

Two variables decide which figures are plotted for which dataset: dataname and doplot. The variable dataname defines the path to the dataset that should be used to produce the figures, while the value doplot defines which figure to reproduce. By setting doplot="fig1", Figure 1 is reproduced, while doplot="fig7" and doplot="fig9" reproduce Figures 7 and 9, respectively. For all other values for doplot, Figures 4, 5, 6, 8 and 11 are reproduced. We decided not to include a script to plot Figure 3 because the data the climatology is based on is owned by GeoSphere Austria - the agency operating the permanent weather station. Further, no script for reproducing Figure 10 is included because it was not created within the framework of Python.

Geofiles

The files included in geofiles.zip are needed to plot Figure 1 and Figure 2, although not of particular interest on their own. Included are output files from geogrid.exe, whicih are necessary to plot the domains overview (Figure 1), as well as an orthophoto (orthophoto.tif) and high-resolution digital elevation model (topo_hr.tif) which are both based on data from Land Tirol.

The table below showcases the total number of homes sold for each zip code in Snow Lake, Arkansas. It's important to understand that the number of homes sold can vary greatly and can change yearly.

In 2023, there were 25 recorded snow days in Seoul, South Korea, six less than during the previous year. 2012 marked a decade-high at 34 recorded snow days in the city.

Data set containing the list and location of snow disposal sites used by the City of Montreal during snow loading periods, including information on each site. The packages on the snow removal sectors, the contracts and transactions are also available on the portal.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

In 2024, the average air temperature in Japan's capital reached around 17.6 degrees Celsius. Tokyo's annual mean air temperature increased by four degrees Celsius since 1900, showing the progress of global warming. Weather in Tokyo Tokyo lies in the humid subtropical climate zone. It is affected by the monsoon circulation and has mild, sunny winters and hot, humid, and rainy summers. In most of Japan, the rainy season lasts from early June to mid-July. Furthermore, heavy rainfall is often caused by typhoons, which develop over the Pacific Ocean and regularly approach the archipelago between July and October. In recent years, the Kanto region, including Tokyo Prefecture, was approached by at least two typhoons each year. Since the winters are rather mild in Tokyo, the capital city does not often see snowfall and the snow rarely remains on the ground for more than a few days. Effects of global warming in Japan The increasing air temperature is one of the main consequences of global warming. Other effects are increased flooding frequency and a rise in sea levels due to melting ice caps. Global warming has already influenced Japan's climate in recent years, resulting in more frequent heat waves as well as increased annual rainfall. These weather changes can intensify natural disasters such as typhoons and inhibit the growth of crops. To counter global warming, Japan aims to reduce its greenhouse gas emissions by increasing its renewable and nuclear energy share.