Climate change and the South Asian monsoon. Dr Andy Turner*

Transcription

1 7-8 November 2014 ICIMOD, Kathmandu, Nepal Climate change and the South Asian monsoon International Workshop on Water and Air Challenges in the HKH Under Climate and Environmental Change: Dr Andy Turner* Opportunities Using a Transdisciplinary Approach * 7 8 November NCAS-Climate, 2014 University of Reading, UK Organized by International Centre for Integrated Mountain Development (ICIMOD) Supported by Universität Hamburg, Cluster of Excellence Integrated Climate System Analysis and Prediction (CliSAP), University of Reading, Walker Institute Thanks to H. Annamalai, Liang Guo, Richard Levine, Deepthi Marathayil

2 7-8 November 2014 ICIMOD, Kathmandu, Nepal The monsoon in South Asia major modelling challenges and changing climate Dr Andy Turner* * NCAS-Climate, University of Reading, UK Thanks to H. Annamalai, Liang Guo, Richard Levine, Deepthi Marathayil

3 Seasonal 925hPa wind changes and monsoons DJF West African Monsoon Asian Monsoon Austral Monsoon JJA

4 Seasonal daily precipitation changes and monsoons DJF West African Monsoon Asian Monsoon Austral Monsoon JJA

5 Outline Skill of the CMIP3/5 models at monsoon simulation and future projections The Arabian Sea as an example of model error and its impact The role of anthropogenic aerosol emissions in the 20 th century monsoon The HKH at the confluence of tropical monsoons and mid-latitude weather systems

6 Monsoon precipitation biases a large monsoon metrics exercise From Sperber,,Turner et al. (2012), Climate Dynamics. Large range of skill at simulating the mean monsoon precipitation in CMIP3 and CMIP5 models. Mean JJAS precipitation (left) and bias versus GPCP obs (right)

7 Multi-model mean monsoon precipitation biases in CMIP/5 CMIP3 and CMIP5 models show large dry biases over India but wet biases over the WEIO and Maritime Continent in boreal summer. Beware counterintuitive colour scale. Sperber, Annamalai, Kang, Kitoh, Moise, Turner, Wang and Zhou (2012) Climate Dynamics.

8 Multi-model mean circulation biases in CMIP3/5 Weak Somali Jet in CMIP3 and CMIP5. Mean JJAS 850hPa winds (left) and bias versus ERA-40 (right)

9 Relationship between circulation and precipitation biases in CMIP3/5 Strong evidence for connection between biases in monsoon circulation and precipitation. Scatter diagram of pattern correlations of simulation of JJAS precipitation & 850hPa winds Sperber, Annamalai, Kang, Kitoh, Moise, Turner, Wang and Zhou (2012) Climate Dynamics.

10 Future projections of mean monsoon precipitation JJAS precipitation change for CMIP3 models: 1pctto2x-picntrl 22xCMIP3 MME mean 4xCMIP3 Mean JJAS precipitation is shown to increase for much of South, Southeast and East Asia with increasing CO 2 concentrations. From Turner & Annamalai (2012) Nature Climate Change 2

11 Reasonable model future projections support multi-model mean JJAS precipitation change for CMIP3/5 models: 1pctto2x-picntrl 4xCMIP5 4xCMIP3 Four CMIP5 models selected according to their pattern correlation for monsoon precipitation over South, Southeast and East Asia monsoon domain. (CCSM4, CNRM-CM5, GFDL-CM3, NorESM1-M.)

12 Uncertainties in IPCC 1pctto2x mean projections Large uncertainty in mean JJA rainfall change over Asian monsoon? from Turner & Slingo (2009b) Atmos. Sci. Lett. 10

13 Mean monsoon change: summary CMIP (IPCC) models offer huge diversity of skill at simulating the South Asian monsoon Projections of mean monsoon rain under increased GHG forcing are generally positive but with large diversity over the magnitude and spatial pattern Increases occur owing to the enhanced availability of moisture over the warmer Indian Ocean Projections remain consistent when best models are selected

14 Model uncertainty: Arabian Sea as an example (Levine & Turner 2013, Clim. Dyn.) CMIP5 models with cold springtime Arabian Sea a weakened seasonal cycle of rainfall and lower absolute rainfall levels under warming scenario (RCP8.5)

15 Biases in the monsoon onset Onset pentad using method of Wang & Linho. Delayed onset in CMIP3 and CMIP5 models. From Sperber et al. (2013) Clim. Dyn.; also Sperber & Annamalai (2014) Clim. Dyn.

16 Biases in the monsoon onset Rainfall, wind KE, circulation indices all show later monsoon onset in coupled GCMs (dotted) compared to AGCMs (solid) CMIP5 models all show delayed northward advance of monsoon belt compared to AGCM version and later onset at each gridpoint Turner (2014, in preparation)

17 Some issues relating to model bias Coupled models as used in IPCC (CMIP5) still suffer large biases for the monsoon but they are out best option Example SST cold Arabian Sea SST biases lead to: - weakened monsoon rainfall - Delayed monsoon onset - Reduced response to GHG increases See Levine & Turner (2012, Climate Dynamics), Levine et al. (2013, Climate Dynamics), Marathayil et al., (2013, Environ. Phys. Letts.) for more information

18 The uncertain role of aerosol for the South Asian monsoon All: 25 GCMs BL: 14 GCMs with aerosol indirect effects BR: 11 GSMs with direct effect only. Area mean is calculated over South Asia (5-35N,65-90E). Seasonal (JJA) South Asia precipitation, CMIP5 historical runs: timeseries and late 20 th century minus pre-industrial Guo, Turner & Highwood (2014, Atmos. Chem. & Phys. Disc.)

19 The uncertain role of aerosol for the South Asian monsoon All: 25 GCMs BL: 14 GCMs with aerosol indirect effects BR: 11 GSMs with direct effect only. Area mean is calculated over South Asia (5-35N,65-90E). Guo, Turner & Highwood (2014, Atmos. Chem. & Phys. Disc.)

20 Some issues in monsoon-aerosol modelling Complexity of aerosol effects included in models matters Aerosol emissions have been large enough to reduce monsoon rainfall over South Asia counteracting GHG Both local and large-scale (hemispheric) effects are important Future RCPs rely on decreases in future aerosol emissions. Can we rely on them? See Guo, Turner, Highwood, ACPD (soon) for details Irrespective of climate impacts, aerosol represent an air quality problem

21 Key role of mid-latitude interactions with the tropical monsoons: Pakistan 2010 vs. UA June 2013 upper level streamfunction July 2010 upper level streamfunction Upper level flow June 2013 Upper level flow July 2010 Charts from Climate Prediction Center/NCEP and University of Reading archive of ECMWF analysis L H L

22 The end Thank you! See Turner & Annamalai (2012) Nature Climate Change 2: or