Stochastic weather generators and modelling climate change. Mikhail A. Semenov Rothamsted Research, UK

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1 Stochastic weather generators and modelling climate change Mikhail A. Semenov Rothamsted Research, UK

2 Stochastic weather modelling Weather is the main source of uncertainty Weather Management Crop model Environment

3 Probabilistic predictions Traditional Probabilistic

4 Assessment of risk Long weather time-series suitable for the risk assessments frequency penalty risk

5 Stochastic Weather Generator Weather generator is a stochastic numerical model to generate daily weather series statistically identical to observed. It can be used to generate long weather time-series suitable for risk assessment; to provide the means of extending the simulation of weather to unobserved locations; to serve as a computationally inexpensive tool to produce climate change scenarios.

6 Stochastic Weather generators Parametric (WGEN) small set of parameters, prior assumptions on distributions Semi-parametric (LARS-WG) small set of parameters, no prior assumptions on distributions Non-parametric (Bootstrap resampling) Original dataset used to resample time series

7 Bootstrap resampling Parametric Semi-parametric (mean, sd)

8 LARS-WG stochastic weather generator Generates precipitation, min and max temperature and radiation Modelling of precipitation event is based on wet/dry series Semi-empirical empirical distributions are used for precipitation amounts, dry/wet series and radiation Temperature and radiation are conditioned on the wet/dry status of a day and cross-correlated correlated

9 Modelling precipitation Markov chain model.25 Series model Pdd dry probability dry series.5 1 wet wet series Pww probability

10 Modelling temperature

11 WG performance and comparison: LARS-WG and WGEN Jokioinen Moscow Boise Bismarck Indianapolis Caribou Rothamsted Wageningen Munich Debrecen Tucson Mobile Bologna Montpellier Seville Athens

12 Comparison between observed and LARS-WG generated weather Site Series Rain Month mean Month var Athens Bismarck 2 Boise 1 2 Bologna 1 Caribou Debrecen 3 Indianapolis 2 Jokioinen 4 Mobile Montpellier 1 Moscow 2 Munich 3 Rothamsted 4 Seville 1 Tashkent Tucson 1 1 Verhojansk 3 Wageningen 5 Number of significant differences between observed and synthetic distributions of weather variables

13 Spatial interpolation of LARS-WG 138 sites with daily weather

14 Spatial interpolation of LARS-WG 138 sites with daily weather Monthly mean precipitation for sites 138 sites with daily weather 2376 sites with monthly means Local interpolation Global adjustment

15 Publications Semenov M.A. and Barrow. E.M. (1997) Use of a stochastic weather generator in the development of climate change scenarios Climatic Change, 35: Semenov M.A., Brooks R.J., Barrow E.M. and Richardson C.W (1998) Comparison of the WGEN and LARS-WG stochastic weather generators in diverse climates. Climate Research 1: Semenov M.A. and Brooks R.J. (1999) Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. Climate Research 11: WWW site: model/larswg.php

16 The greenhouse effect

17 Temperature and CO 2 over 4 years (from Vostok ice core)

18 Departures in temperature from averages.8.6 Departures in temperature from average Reconstructed Observed

19 Modelling Climate Change: HADCM3 Conceptual scheme

20 Downscaling or Upscaling? GCM predictions Crop modelling ~3x3 km 2 spatial 1km monthly temporal daily 5 1

21 Downscaling methods from GCMs Regional limited-area models Downscaling with a stochastic weather generator

22 Spatial downscaling: HadRM climate regional model HadCM3 global model HadRM regional model

23 Sensitivity of crop models to changes in temperature mean and variability Changes in mean yield, % Changes in yield CV, % T+3 2*sd T&sd AFRC CERES Nwheat -2-4 T+3 2*sd T&sd AFRC CERES Nwheat

24 Permutation of LARS-WG parameters controlling mean and variability of daily temperature

25 Climate Change Scenarios: high temporal ands spatial resolutions GCM RegCM Stochastic Weather Generator Crop Models Observations low resolution, 3 km high resolution, 1 km

26 Effect of changes in climatic variability on simulated grain yield (Nature, 1999) Yield CV base no Var Var base no variability with variability

27 Low probability high impact event

28 Global warming?

29 Publications Semenov, M.A. and Barrow, E.M., Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change, 35: Semenov, M.A. and Porter, J.R., Climatic variability and the modelling of crop yields. Agricultural and Forest Meteorology, 73: Semenov, M.A., Wolf, J., Evans, L.G., Eckersten, H. and Iglesias, A., Comparison of wheat simulation models under climate change.2. Application of climate change scenarios. Climate Research, 7: Porter JR & Semenov MA (25) Crop responses to climatic variability. Philosophical Transactions of the Royal Society. B, 36 (1463), Ewert, F. et al., 22. Effects of elevated CO2 and drought on wheat: testing crop simulation models for different experimental and climatic conditions. ions. Agriculture Ecosystems & Environment, 93(1-3): 3): Jamieson, P.D. et al., 2. Modelling CO2 effects on wheat with varying nitrogen supplies. Agriculture Ecosystems & Environment, 82(1-3): Wolf, J., Evans, L.G., Semenov, M.A., Eckersten, H. and Iglesias, A., Comparison of wheat simulation models under climate change.1. Model calibration and sensitivity analyses. Climate Research, 7: