* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Climate Change Risk and Uncertainty Exercise 1: Temperature
Media coverage of global warming wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Global warming wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Climate change feedback wikipedia , lookup
Numerical weather prediction wikipedia , lookup
Climate change and poverty wikipedia , lookup
Solar radiation management wikipedia , lookup
Climate change in the United States wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Years of Living Dangerously wikipedia , lookup
Effects of global warming on Australia wikipedia , lookup
Climate change, industry and society wikipedia , lookup
Global Energy and Water Cycle Experiment wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Global warming hiatus wikipedia , lookup
Atmospheric model wikipedia , lookup
Climate sensitivity wikipedia , lookup
IPCC Fourth Assessment Report wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Climate Change Risk and Uncertainty Exercise 1: Temperature Increase in New York City by Prof. Reto Knutti & Prof. David Bresch Tutorials Anina Gilgen ([email protected]) Kathrin Wehrli ([email protected]) Martin Stolpe ([email protected]) Department of Environmental System Sciences - ETH Zurich Zurich, February 27, 2017 1 Introduction In this exercise, we will examine the annual mean near surface temperatures in New York City (40.99°N/74.56°W). The observational time series we will analyse is based on dierent meteorological stations located in New York and is quality-controlled (Figure 1). For more information about the individual stations and the adaptations conducted to homogenize the data, you can visit the following webpage http://berkeleyearth.lbl.gov/station-list/?phrase=new+york. In the rst part of the exercise, we will create a toy model based on the observed time series. After that, we will compare the observations and our toy model with results from two global climate models (CESM and MPI-ESM). In the end, we will calculate impact costs associated with future temperature increase and compare them to mitigation costs. There are some voluntary questions denoted with a *, which give you extra points. Feel free to ask us any question during the tutorials or by email. For many of the questions dierent possible solutions exist. Hence, it is important that you explain and reason your solution process. 12 Observed Temperatures in New York 11.5 Temperature (° C) 11 10.5 10 9.5 9 8.5 8 annual 11-year moving average 7.5 1800 1820 1840 1860 1880 1900 1920 1940 1960 1980 2000 Year Figure 1: Observed annual mean temperatures in New York from 1800 to 2012 (in blue) and after smoothing with a 11-year moving average (in red). In the exercise we will mainly focus on the observations since 1960. Source: 99N-74. 56W . 1 http: // berkeleyearth. lbl. gov/ locations/ 40. Toy Model Download the mat-les (observations_new_york_short.mat ; cesm_new_york.mat ; mpi_new_york.mat ) from the lecture homepage and store them in your local MATLAB folder. Load the observations (by double-clicking or by using the function load) in MATLAB. This loads six matrices: the annual mean surface temperature (tas) and the years for the observations, for CESM, and for MPI-ESM. As you can see, the les span dierent time periods. Note that tas_cesm contains 29 rows. Each row is one ensemble member; more details will follow in Section 2. 1. In the following, we only use the observations from 1960 to (and including) 2000 using the le observations_new_york_short.mat. Plot the observations for this time period (plot). 2 2. Estimate the trend over this period using linear regression (e.g. 3. Is the trend signicant on the 5 %-level? (e.g. polyt). regstats) 4. Since when is the trend signicant? Hint: Starting from 1960, analyse the trend over longer and longer time periods. detrend) to get the noise. 5. Detrend the observations ( How large is the standard deviation of the noise? 6. Is the noise normally distributed? Create a qq-plot ( qqplot), hist), a histogram ( and a Tuckey-Anscombe plot (plotting residuals against tted values) to answer this question. So far, we have estimated the trend as well as the noise of the time series. From that, we will now create a simple toy model: T = a + b · t + µ, where T is the estimated temperature, µ time, and is the noise. a and b are the coecients of the linear regression, t is In your toy model, you can assume that the noise is normally and randomly distributed, i.e. use the function normrnd to generate the (white) noise with the previously estimated standard deviation. 7.* Some time series are serially correlated, meaning that the data points of the time series linearly depend on previous values. Examine if the noise of this time series is auto-correlated (e.g. use autocorr and parcorr). If the is noise is serially correlated, how would you adjust the toy model to account for it? 8. Using the toy model, calculate at least 1000 simulations for the time period 1960 to 2012 and plot them into one gure. Use your ≥1000 simulations to answer the following questions. 9. Calculate and plot also the 5th, 50th and 95th percentiles as well as the mean of these simulations. 10. Now compare your model results with observations (especially with the time period from 2001 to 2012, which was not used to create your toy model. Use the le observati- ons_new_york_full.mat that contains observations until 2012). Comment on your results. 11. Use your toy model to estimate the probability that the annual mean temperatures exceed ◦ 11.5 C for each year from 1960 to 2012. How does the probability change over time? 12. What are limitations of this toy model? Name and shortly explain at least two of them. 2 Comparison with Climate Model Data Earth system models are used to simulate the climate system, including the atmosphere, oceans, land surface, sea ice and the interactions between them. Now you will examine the output of two 1 such state-of-the-art climate models, the Community Earth System Model Version 1 (CESM1 ) 2 and the Max Planck Institute Earth System Model (MPI-ESM-LR ). Observed changes in the greenhouse gas concentrations, aerosols, the solar activity, the land use and further forcing agents are used to drive these climate models. For the future the models are forced with the business as usual scenario RCP8.5 that assumes strong increases in the concentration of greenhouse gases 1 Meehl et al. (2013), Climate Change Projections in CESM1(CAM5) Compared to CCSM4, in Climate, http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-12-00572.1 2 Journal of Giorgetta et al., (2013), Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5, in Journal of Advances in Modeling Earth Systems, http://onlinelibrary.wiley.com/doi/10.1002/jame.20038/full 3 3 during the 21st century . To quantify the role of unpredictable internal variability, CESM1 is run 29 times from 1920 to 2100 with sightly diering atmospheric initial conditions (e.g. Deser et al. (2012) 4 used a large ensemble to address similar questions as you will). You nd each of the 29 simulations in cesm_new_york.mat. MPI-ESM-LR spans the period from 1850 to 2100 and consists of three ensemble members. In mpi_new_york.mat you nd the mean time series of these three members. 1. Plot the 29 CESM1 ensemble members, the ensemble mean and median, the MPI-ESM-LR simulation, together with the observations for the period 1960 to 2000. 2. Compare the observations with the model results. a) Compare the mean climate in the models and observations. Comment on your results and give at least one possible reason for dierences. b) Compare the simulated with observed interannual variability. Comment on your results and the assumptions you used to estimate the interannual variability from observations and model. c) Compare the simulated interannual variability of CESM1 and MPI-ESM-LR. Comment on your results. d) Compare the simulated trends by CESM1 from 1960 to 2000 with the observed trends over the same period (e.g. look at histograms of the trends). Fit an appropriate distribution to the modeled trends. Is there a signicant dierence between the modeled and observed temperature trends? Comment on your results. e*) Compare the simulated temperature trends of CESM1 and MPI-ESM for dierent periods (you have free choice). Are there signicant dierences? What is the inuence of the trend length? Give at least one possible reason for dierences. 5 3.* Detection of a climate signal . As you have seen, there is both interannual variability (i.e. noise) and a long-term trend in the observations and the model simulations. It takes a while until the warming is strong enough so that it emerges from the natural range of variability. The concept of Time of Emergence (ToE) relates the signal, S, to the noise, N: T oE = Here, σ S S = N 2σ denotes the internal variability you estimated earlier from the observed/modeled temperatures. ToE is reached, when S is ≥ 2σ and does not fall into the band of internal variability again. Figure 2 illustrates the concept of ToE. a*) Has ToE already occurred within the observations? If yes, when? b*) When does ToE occur in each of the 29 CESM1 ensembles? Use the simulated internal variability for σ. How many years of spread occur between the members with the earliest and latest ToE? Use the full CESM simulations until 2100 to answer this question. 3 Representative Concentration Pathway (RCP) that reaches a radiative forcing of 8.5 W m−2 in 2100 compared to pre-industrial values. For comparison: A doubling of the CO2 concentration causes a radiative forcing of about 3.7 W m−2 . 4 Deser et al., (2012), Communication of the role of natural variability in future North American climate, in Nature Climate Change, http://www.nature.com/nclimate/journal/v2/n11/full/nclimate1562.html 5 Mahlstein et al. (2011), Early onset of signicant local warming in low latitude countries, in Environmental Research Letters, http://iopscience.iop.org/article/10.1088/1748-9326/6/3/034009 4 Time of Emergence of a Climate Signal 1.2 annual time series 1 Temperature Anomaly (°C) 0.8 0.6 0.4 0.2 0 −0.2 −0.4 Figure 2: ToE 1860 1880 1900 1920 1940 Year 1960 1980 2000 2020 Concept of the Time of Emergence. The grey shaded area indicates the range of internal variability (the noise) and the time series the signal. Around 1970 the observed temperatures exceed the internal variability permanently. Also before 1970 the signal is sometimes larger than the internal variability, but then falls back into the internal variability envelope within a few years. 3 Impact and Mitigation Cost 6 Imagine you are one of the last commercial urban apple tree farmers in New York . At the moment, you breed the apple variety Gala. Since Gala is susceptible to the temperature-dependent apple scab disease (Apfelschorf ), you fear that climate change might lead to more frequent outbreak of this disease and therefore to a smaller harvest. Assume that your monetary los- ses associated with this reduced agricultural productivity can be described with the following function: T < 11 ◦C : no ◦ loss ◦ 11 C < T < 12 C : loss = 10000 USD T > 12 ◦C : loss = 10000 USD ◦ C−1 · (T − 11 ◦C) + (T − 12 ◦C) · 50000 USD ◦ C−1 You consider now to cultivate the more resistant apple variety Resi. With Resi, apple scab is not an issue and you expect no temperature-dependent loss in yield. However, to switch from Gala to Resi, you have additional initial investment costs of 10000 USD. Use your toy model to answer the following questions. 1. At which temperature will the investment costs be outweighed? 6 Urban farming: The green answer urban-farming-green-answer-to-city-growth/ to 5 city growth?, https://share.america.gov/ 2. Which apple variety is more protable in 2030, 2050, and 2070? distribution of temperature into account for your calculations (e.g. neglect discounting and interest rates. 6 Take the probability pd and cdf ). You can