Understanding Future Changes in Tropical Rainfall and its Variability
Lead Research Organisation:
University of Exeter
Department Name: Mathematical Sciences
Abstract
Changes in regional tropical rainfall under anthropogenic forcing are one of the least certain areas of current climate projections, but have the potential to have enormous impacts on agriculture, food and water security and biodiversity in many countries. The patterns of rainfall change in model projections are closely tied to changes in patterns of Sea Surface Temperature (SST). Large-scale coupled atmosphere ocean processes are clearly at play and are related to, but are not direct analogues of, those processes responsible for interannual variability such as the El Niño Southern Oscillation (ENSO).
Uncertainty in future changes is likely to be related to common present-day climate model biases, but this relationship is not well understood. Progress is being made on understanding the mechanisms that drive the patterns of mean rainfall change in climate model projections - both in the ensemble mean projection and the inter-model uncertainty. Recent work has also established, for the first time, a change in the intensity and location of rainfall anomalies during ENSO events in climate model projections. This change in the character of ENSO precipitation variability is intimately linked to a change in the mean state SST patterns.
This PhD project will seek to improve our understanding of future changes in tropical rainfall and its seasonal variability. The main research questions are:
1) How are changes in mean tropical precipitation patterns across the tropics related to changes in interannual/seasonal rainfall variability, and modes of variability such as ENSO and the India Ocean Dipole (IOD)? How do coupled atmosphere-ocean processes work across different timescales?
2) Can we use observations of present-day tropical variability, and analysis of climate model biases, to evaluate likely errors in future projections of mean rainfall change? Can we narrow-down or constrain the range of responses we see across models?
3) How do changes in mean state rainfall in the tropics impact on natural modes of variability like ENSO? Do uncertainties in mean climate change lead to uncertainties in changes in variability?
Work will involve analysis of model projections from the CMIP5 database, observational datasets and additional model experiments focused on ENSO changes performed using Met Office Hadley Centre models.
Chadwick, R et al., Spatial Patterns of Precipitation Change in CMIP5: Why the Rich Do Not Get Richer in the Tropics. Journal of Climate, 26(11): 3803-3822, 2013.
Xie, S et al., Global Warming Pattern Formation: Sea Surface Temperature and Rainfall.
Journal of Climate, 23(4): 966-986, 2010.
Uncertainty in future changes is likely to be related to common present-day climate model biases, but this relationship is not well understood. Progress is being made on understanding the mechanisms that drive the patterns of mean rainfall change in climate model projections - both in the ensemble mean projection and the inter-model uncertainty. Recent work has also established, for the first time, a change in the intensity and location of rainfall anomalies during ENSO events in climate model projections. This change in the character of ENSO precipitation variability is intimately linked to a change in the mean state SST patterns.
This PhD project will seek to improve our understanding of future changes in tropical rainfall and its seasonal variability. The main research questions are:
1) How are changes in mean tropical precipitation patterns across the tropics related to changes in interannual/seasonal rainfall variability, and modes of variability such as ENSO and the India Ocean Dipole (IOD)? How do coupled atmosphere-ocean processes work across different timescales?
2) Can we use observations of present-day tropical variability, and analysis of climate model biases, to evaluate likely errors in future projections of mean rainfall change? Can we narrow-down or constrain the range of responses we see across models?
3) How do changes in mean state rainfall in the tropics impact on natural modes of variability like ENSO? Do uncertainties in mean climate change lead to uncertainties in changes in variability?
Work will involve analysis of model projections from the CMIP5 database, observational datasets and additional model experiments focused on ENSO changes performed using Met Office Hadley Centre models.
Chadwick, R et al., Spatial Patterns of Precipitation Change in CMIP5: Why the Rich Do Not Get Richer in the Tropics. Journal of Climate, 26(11): 3803-3822, 2013.
Xie, S et al., Global Warming Pattern Formation: Sea Surface Temperature and Rainfall.
Journal of Climate, 23(4): 966-986, 2010.
Organisations
Publications
Todd A
(2018)
Diagnosing ENSO and Global Warming Tropical Precipitation Shifts Using Surface Relative Humidity and Temperature
in Journal of Climate
Timmermann A
(2018)
El Niño-Southern Oscillation complexity.
in Nature
| Description | A key finding from the research funded by this grant is a novel method for diagnosing tropical rainfall changes, as published in Todd et al. (2018). This method suggests tropical rainfall shifts are largely linked with surface air temperature and relative humidity changes. This link is supported by physical mechanisms and is evident in both observed present day climate variability and simulated twenty-first century climate projections. Therefore, this work improves our understanding of projected tropical climate changes under anthropogenic global warming. An important new research question is to identify the robust features of projected relative humidity changes, in order to improve our confidence in projected rainfall changes. Todd, A., M. Collins, F.H. Lambert, and R. Chadwick, 2018: Diagnosing ENSO and Global Warming Tropical Precipitation Shifts Using Surface Relative Humidity and Temperature. J. Climate, 31, 1413-1433. |
| Exploitation Route | Future work could involve validating this method for diagnosing tropical rainfall changes on newly available observational climate model datasets. With further refinement, the method could be applied to identify which features of tropical climate change are physically plausible and therefore help to reduce uncertainty for policymakers in these high impact areas. |
| Sectors | Environment |
| URL | https://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-17-0354.1 |