Process-Based Emergent Constraints on Global Physical and Biogeochemical Feedbacks
Lead Research Organisation:
UNIVERSITY OF EXETER
Department Name: Mathematical Sciences
Abstract
We use complex computer models of the climate system to make predictions of how climate will change in the future under scenarios of global warming. Despite the use of large supercomputers, we have to make approximations in building climate models so that we can produce century-long climate simulations. These approximations lead to 'errors' in models i.e. we know that climate models cannot reproduce exactly the observations we have made of past average climate and climate change. Climate model errors lead to uncertainties when we make predictions.
We constantly strive to build new and better climate models in order to reduce uncertainty in predictions. We are guided by our past experience and intuition in targeting resources in specific areas of model improvement. But can we be more efficient? If we knew which of the many errors that exist in our ability to simulate present day climate and climate change are most important in determining the uncertainties in future predictions, could we better use our limited resources to improve models more quickly?
In this proposal we aim to develop a generic technique to better target resources on improving climate models. The central concept, which we call a process-based emergent constraint, is to link errors in climate models to uncertainties in predictions. We will target three different areas; water vapour and lapse rate (the rate of change of atmospheric temperature with altitude), low-level clouds and processes that affect the rate of exchange of carbon in the land-surface and vegetation with the atmosphere. All these processes are know to have a leading-order influence on uncertainty in global temperature predictions and their regional consequences. We aim to exploit state-of-the-art model simulations and data from new observational platforms or data from existing observations that we will process in novel ways. There will be a focus on data from satellites. A key aim of our project is to identify processes that are responsible for both errors in models and uncertainties in predictions and to develop measures (metrics) that can be used to evaluate those processes in models.
The project will provide multiple examples of how to target climate model improvement on processes that really matter for projections. The ultimate aim is to reduce uncertainty in predictions. However, in addition, the project will provide information on how to better target new observations of specific climate processes. We aim to make recommendations for future observational programmes (or the continuation of existing programmes) that will lead to more rapid progress in climate modeling and prediction.
We constantly strive to build new and better climate models in order to reduce uncertainty in predictions. We are guided by our past experience and intuition in targeting resources in specific areas of model improvement. But can we be more efficient? If we knew which of the many errors that exist in our ability to simulate present day climate and climate change are most important in determining the uncertainties in future predictions, could we better use our limited resources to improve models more quickly?
In this proposal we aim to develop a generic technique to better target resources on improving climate models. The central concept, which we call a process-based emergent constraint, is to link errors in climate models to uncertainties in predictions. We will target three different areas; water vapour and lapse rate (the rate of change of atmospheric temperature with altitude), low-level clouds and processes that affect the rate of exchange of carbon in the land-surface and vegetation with the atmosphere. All these processes are know to have a leading-order influence on uncertainty in global temperature predictions and their regional consequences. We aim to exploit state-of-the-art model simulations and data from new observational platforms or data from existing observations that we will process in novel ways. There will be a focus on data from satellites. A key aim of our project is to identify processes that are responsible for both errors in models and uncertainties in predictions and to develop measures (metrics) that can be used to evaluate those processes in models.
The project will provide multiple examples of how to target climate model improvement on processes that really matter for projections. The ultimate aim is to reduce uncertainty in predictions. However, in addition, the project will provide information on how to better target new observations of specific climate processes. We aim to make recommendations for future observational programmes (or the continuation of existing programmes) that will lead to more rapid progress in climate modeling and prediction.
Planned Impact
The main beneficiaries of the research, outside the academic community, will be those involved in setting climate change policy in both the adaptation and mitigation arenas. Examples include Civil Servants from the Department of Energy and Climate Change, the Department of Food and Rural Affairs and the Department for International Development (DECC/DEFRA/DFID). They will benefit from improved knowledge of the validity of model projections of future climate change and from improved understanding of the potential to reduce uncertainty in projections.
Organisations
Publications
Lambert F
(2014)
Regional variation of the tropical water vapor and lapse rate feedbacks
in Geophysical Research Letters
Ferraro A
(2015)
A hiatus in the stratosphere?
in Nature Climate Change
Anav A
(2015)
Spatiotemporal patterns of terrestrial gross primary production: A review
in Reviews of Geophysics
Good P
(2015)
Are strong fire-vegetation feedbacks needed to explain the spatial distribution of tropical tree cover?
in Global Ecology and Biogeography
Collins M
(2015)
Physical Mechanisms of Tropical Climate Feedbacks Investigated Using Temperature and Moisture Trends*
in Journal of Climate
Ferraro A
(2016)
Quantifying the temperature-independent effect of stratospheric aerosol geoengineering on global-mean precipitation in a multi-model ensemble
in Environmental Research Letters
Harper A
(2016)
Improved representation of plant functional types and physiology in the Joint UK Land Environment Simulator (JULES v4.2) using plant trait information
in Geoscientific Model Development
Hopcroft P
(2017)
Multi vegetation model evaluation of the Green Sahara climate regime
in Geophysical Research Letters
Lambert F
(2017)
Land-Ocean Shifts in Tropical Precipitation Linked to Surface Temperature and Humidity Change
in Journal of Climate
Harper A
(2018)
Vegetation distribution and terrestrial carbon cycle in a carbon cycle configuration of JULES4.6 with new plant functional types
in Geoscientific Model Development
Good P
(2018)
Recent progress in understanding climate thresholds Ice sheets, the Atlantic meridional overturning circulation, tropical forests and responses to ocean acidification
in Progress in Physical Geography: Earth and Environment
Description | It is difficult to tie down feedbacks in the climate system from water vapour and vertical temperature changes |
Exploitation Route | Too early to say |
Sectors | Environment |