Using a library of downscaled climate projections to teach climate change analysis

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Using a library of downscaled climate projections to teach climate change analysis Eugene Cordero, Department of Meteorology San Jose State University Overview of Dataset Climate change activity

Applications Courses General education (hands on!) Climatology Climate Change Statistics Research

CMIP3 Dataset Coupled model intercomparison project third one Used to support the IPCC Fourth Assessment Report (2007) 25 Atmosphere-Ocean Global Climate Models Multiple time output (3hr, daily, monthly) Multiple variables (standard variables, extremes) Multiple simulations (scenarios, 2xCO2, 4xCO2 etc.)

Temperature: the last 125 years

Temperature: the last 125 years

The IPCC Special Report on Emissions Scenarios (SRES) Climate models require boundary conditions, prescribed levels of future greenhouse and other trace gases and aerosols. But this requires knowledge about how human societies might evolve in the future. To address this need, a number of possible pathways for societal development, and their implications for the composition of the atmosphere, have been explored in the IPCC SRES.

IPCC AR4: Some findings Global mean surface temperature projections, based on various scenarios.

Temperature: the last 125 years CMIP3 Dataset: 35 TB: Downloaded over 300 TB

Statistical Temperature: Downscaling the last 125 years

Statistically Downscaled CMIP3 Climate 112 Simulations (20 th century, A1B, B1, A2 and ensemble members) Monthly averaged data (1950-2100) Downscaled to the Continental USA 1/8 degree resolutions (~ 12 km) Temperature and precipitation Web-based interface Projections

Library http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/ Google: Ed Maurer data Credit: Prof. Ed Maurer, Santa Clara University http://www.engr.scu.edu/~emaurer/

Library is simple to use easy to access data! Useful for various levels of courses Three examples Classroom Activities 1. General education course (Excel) 2. Statistics course using Matlab/IDL/IDV 3. Climate Course (Excel/Matlab/IDL)

Example 1 Course: General education (non science) Plot out the local temperatures for your hometown from one model using three scenarios for the years 1950-2100 Plot out the average July temperatures in the 1950s compared to the 2050s Simple plots (e.g. Excel)

Precipitation

Example 2 Course: Statistical climatology Download a single model of temperatures for the state of your choice - Compute and plot MAM mean temperatures for 1950-2000 and 2050-2099 - Determine if those means are statistically different - Compute and plot state average temperature - Compute trend and trend uncertainty

Course: Climate change course Project Example 3 Use CMIP3 library to study how temperature and precipitation may change in the 21 st century Compute trends and trend uncertainties in temperature Compare extremes in temp and precip. Explore relationship between temperature change and precip change

Precipitation and Temperature

For single point - CSV files ideal for Excel, easy and fast For areas - NetCDF files Arrays Array experience - Temp1(1800,33,49): 1800 months, 33 lats, 49 lons - Temp2(3,1800,33,49) - now with three scenarios - Exercise: convert Temp2 to Temp3 - Temp3(3,12,150,33,49) or (scenario,month,yr,lat,lon)

Library will calculate area and time averages Students can do these calculations themselves Missing data (-999.0) for areas in ocean For example Data experience - Calculate California average annual temperatures for the 21 st century - Remove missing data - Compute annual mean

Other Useful Datasets Global Dataset (downscaled) Monthly Temp and Precip http://www.engr.scu.edu/~emaurer/global_data/ USA Observations (1950-1999) Daily and Monthly Data (Temp and Precip) http://www.engr.scu.edu/~emaurer/data.shtml

Today s activity Using the climate model library, answer the following questions. For your favorite town or state 1.Plot out your data 1950-2100 2.Estimate by how much January temperatures will change between the 1990s compared to the 2090s for the A1B scenario. 3.In the 2090s, estimate the difference in July temperatures between the high emission scenario and the low emission scenario.