Change the atmospheric fraction lever to see how this influences your results. Past emissions alone are unlikely to raise global-mean temperature to 1.5C.
To do this calculation, we've assumed that the fraction of emitted CO2 which goes into the atmosphere remains constant at about 0.44, but climate models suggest that this fraction is likely to increase for high-emission pathways. Suppose we have a netCDF or xarray.Dataset of monthly mean data and we want to calculate the seasonal average. You may need to change the path to rasm.nc below. Author: Joe Hamman The data used for this example can be found in the xarray-data repository.
#AVERAGE WEATHER CALCULATOR SERIES#
These are "back of the envelope" calculations which don't involve the use of any climate models, just the basic physics of energy and heat transfer. Calculating Seasonal Averages from Time Series of Monthly Means¶. These results are not taken from the more complex IPCC modelling. These are simple calculations using basic physics. The total rise in sea level could therefore be XXXXm. The fraction of this energy which falls on the ocean would cause it to expand, giving about an additional XXXXm of sea level rise. the fraction of this energy which falls on the ice sheets would be enough to melt XXXX of ice by 2100, which would cause about XXXXm of sea level rise. the total amount of extra energy trapped due to human emissions of CO2 since 1870 is about XXXX by 2050 and XXXX by 2100 The order of magnitude impacts of your pathway are: The constant 3.71 comes from the latest IPCC report. We can calculate the extra energy trapped by the extra CO2 generated in your pathway, by using Arrhenius' formula for the rate of heat trapping, Q = 3.71 x ln (C/C_0) Joules of energy per square meter per second. These different approaches have demonstrated that our emissions of greenhouse gases (mainly carbon dioxide) will result in global net warming and therefore local changes to weather patterns. Since then, this principle has been tested by scientists using laboratory experiments and computer simulations. Who also noticed that our burning of fossil fuels would release carbon dioxide and warm the planet. interp_like() which makes it easier to do the interpolation.The principle of the greenhouse effect was first put forward in the 1800s by scientists including Joseph Fourier and Svante Arrhenius , One of the popular methods is to use linear interpolation. Using interpolation, we can change this resolution (increase or decrease). While plotting the contour map, I had to ignore some smaller countries as it is not possible to calculate the average temperature for those. It gets tricky if the country is small such as Vatican City, Monaco, and Pacific islands. This is simple if the region is a big country like India, USA, China, etc. In the case of the NetCDF data I used, we want the region to be bigger than 25 km, at least over one axis, to have a temperature value inside the region. If we have a region big enough, we would see one or more temperature values lie inside the boundary of the region. In other words, we have a temperature value every 25 km in the X and Y direction. At the end of the year you will have 946,080 temperature. Calculate the average temperature anomaly for each square. Divide the planet into a grid of 2,592 squares. Repeat steps 1 and 2 for each day of the year. One degree is roughly 100 km so we can say that our resolution is 25 km. Subtract the temperature you measure at each location from the usual temperature on that day. The data that I used to make the contour plots had a resolution of 0.25 in both directions of the grid. NetCDF data is gridded with equal spacing between the latitudes and longitudes. Interpolation is the process of estimating the values of the unsampled area using the sampled values. This is also called Spatial or Geospatial interpolation. The key to making all this possible is the interpolation of the temperature values in the NetCDF data. Basemap, Plotly - To make the visualizations.Rioxarray and Shapely- To clip/collect the grid points inside a polygon.Xarray - To read and manipulate the NetCDF file.The following python libraries are used for this purpose: To compute the average temperatures, we collect all points lying inside the boundary of the US (for instance) and simply take a mean of all. The figure above shows a bunch of grid points placed over a region (the US and its neighbors). Grid points over Shapefile (Photo by Author)
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