Question: For MACAv1-METDATA,what are the units of precipitation?
With this dataset, we followed a CF convention and are reporting the units of precipitation as a
precipitation flux. For the MACAv2 datasets, we give precipitation in mm as we felt that the extra conversion with
precipitation flux was prone to errors on the user side.
Precipitation flux is the weight of precipitation over a square meter area per sec. To convert this to a depth of daily precipitation(in millimeters), we assumed the density of the precipitation is 1000.0 kg/m3:
daily precipitation (mm) =
(day precipitation flux kg/m2 s)* ( m3/ 1000.0 kg)*(3600 sec/hr) *(24 hr/day)* (1000 mm/m)
Here are some quick guides depending on the time scale you want for your precipitation:
monthly precipitations(mm) = (monthly precipitation flux) * 24*3600*daysInMonths, where
daysInMonths = number of days in that month
daysInMonths = [31 28 31 30 31 30 31 31 30 31 30 31] for the 12 months for all non-HadGEM2 models
daysInMonths = 30 for each month for the HadGEM2 models
annual precipitations(mm): add up all 12 monthly precipitations(mm)
Question: Can I use the gridded MACA data as a station input to my model?
Answer: This is acceptable for rsds and wind variables. However for tasmin/tasmax/pr, you should bias correct the gridded data for the cell which contains your station. If you need assistance with tailoring the downscaled data to stations, please inquire with John Abatzoglou with your options.
Question: Are the MACA data for year 1987 saying something about the weather in the actual year 1987?
Answer: No. The historical period of the MACA data is from 1950-2005, but these years do not correspond to the actual years 1950-2005. What is important here is that these years have the same statistics as the actualy years 1979-2009 (from the training data). The year 1987 of MACA data is not meant to be a hindcast of the weather from that year.
Question: Can I use the MACA data to look at projected future changes for 2040-2044 compared to the years 1990-2000?
GCM experiments are meant to say something about the future projections of climate. To assess climate, you should be taking averages of the weather over at least 30 years of data. So 5 future years is too short to assess the climate and even 10 years of historical is too short.
Also, note that the choice of the baseline period (here 1990-2000) may have other consequences on your comparison. The baseline period is a reference period for calculating the respective future climate change. Choosing Different reference periods for the baseline will result in different projections for change. Further, baseline periods that are chosen as subsets of the period 1950-2005 (i.e. 1990-2000 or 1971-2000) will result in variations in the baseline between the models since the MACA process maps the statistics of the period 1950-2005 to the statistics of the training data. If you wish to avoid inter-model variations in the baseline, choose 1950-2005 as a baseline. As seen in the 'Analysis - Bias Maps' tab on this webpage, this will result in minimal biases between the models and the training data.
Question:I like the projections I've seen with the model CNRM-CM5A. Can I use only this model in my study?
Answer:The intended use of the CMIP5 project is to get statistical information on future climates from the many different models avaiable. You should use as many models as possible in looking at your study in order to get a good siganl on the predicted change for the future (as well as some information on errors or uncertainties between the models).
Question:I aggregated the daily MACA data to annual values but I'm confused that their statistics do not match up with annual values from the training METDATA. Why not?
Answer:The downscaling process is performed on the daily GCM data using the daily training data. This ensures that the distribution of data in 15-45 day windows is mapped to the training data and does not guarantee that the the distribution of annual data matches the training data. Also, each GCM has its own sequencing of daily data, so that aggregations of the daily data to monthly/seasonal/annual values are not likely to match similar aggregations of the training data.