Using simple models to improve understanding of the interactions between components of the climate system
Degree GrantorUniversity of Canterbury
Degree NameDoctor of Philosophy
In this work a hierarchy of simple models are used to gain further understanding of the interaction between atmospheric processes and components, and the impact these processes and components have on the climate system.
A one dimensional energy balance box model is often considered one of the simplest climate models. During this PhD an energy balance model (EBM) of higher complexity, with an atmospheric layer and a surface layer, and a latitudinal resolution of 1_, has been developed, to examine the effects of non-linear interactions between surface albedo, water vapor, cloud cover and cloud albedo (referred to as climate variables) on amplified warming of the polar regions. The EBM is set up so that it is possible to allow one or more climate variables to change with changing temperature or keep them at fixed values from a reference run. Furthermore a method, using a semi-empirical model, for simulating the evolution of the stratospheric ozone layer and its coupling to the climate system was implemented into an existing simple climate model, named Model for Assessment of Greenhouse-gas Induced Climate Change (MAGICC).
For an induced forcing, feedbacks from processes associated with climate variables in the climate system either amplify or damp the warming of the surface temperature. To investigate the non-linear effects feedbacks from surface albedo, water vapour and cloud cover have on the surface temperature the sum of the contributions to surface temperature changes due to any variable considered in isolation is compared to the surface temperature changes from coupled feedback simulations. Our simulations show that the sum of temperature changes is smaller than the temperature changes from coupled feedback simulations, for all simulations. This non-linearity is found to be strongest when all three climate variables are allowed to interact. Surface albedo appears to e the strongest driver of this non-linear behavior, followed by water vapor and clouds. This is because increases in longwave radiation absorbed by the surface, related to increases in water vapor and clouds, and increases in surface absorbed shortwave radiation caused by a decrease in surface albedo, amplify each other. Furthermore, our results corroborate previous findings that while increases in cloud cover and water vapor, along with the greenhouse effect itself, warm the polar regions, water vapor also significantly warms equatorial regions, which reduces polar amplification. Changes in surface albedo drive large changes in absorption of incoming shortwave radiation, thereby enhancing surface warming. Unlike high latitudes, surface albedo change at low latitudes are more constrained. Interactions between surface albedo, water vapor and clouds drive larger increases in temperatures in the polar regions compared to low latitudes. This is in spite of the fact that, due to a forcing, cloud cover increases at high latitudes and decreases in low latitudes, and that water vapor significantly enhances warming at low latitudes.
The effects from reflectivity of clouds (cloud albedo) on the feedbacks from surface albedo, water vapor and cloud cover (referred to as climate variables), and the impact the interaction between these climate variables has on the surface temperature are also examined. Output from simulations with all possible combinations of climate variables (surface albedo, water vapour, cloud cover and cloud albedo) activated or prescribed with values from a reference run are examined. For a doubling of atmospheric concentration of carbon dioxide, the output from the simulations show that there is an increase in cloud albedo in the low and high latitudes due to an increase in cloud temperature (and therefore an increase in the fraction of water droplets compared to ice crystals), and a very small decrease in cloud albedo in mid latitudes due to a decrease in cloud temperature. Changes in surface albedo greatly affect surface and atmospheric temperatures in the high latitudes, and therefore also cloud temperatures and cloud albedo. The cloud albedo in this EBM depends on atmospheric temperature and lapse rate, and therfore there is a large impact from cloud albedo on simulations where the surface albedo feedback is fixed with values from a reference simulation. Furthermore, CO2-induced cloud albedo changes have the greatest effect on simulations where there is little or no interaction between surface albedo, water vapour and cloud cover, because the change in LW radiation emitted from the atmosphere to the surface is then small compared to the change in SW radiation reflected by clouds.
MAGICC is a simple climate model,however of higher complexity than the EBM developed in this PhD project. MAGICC, with the newly implemented stratospheric semi-empirical model, was used to investigate how anthropogenic emissions of greenhouse gases and ozone-depleting substances will continue to affect concentrations of ozone in the stratosphere through the 21st century. While a range of estimates for when stratospheric ozone is expected to return to unperturbed levels is available in the literature, quantification of the spread in results is sparse. Here a first probabilistic study of latitudinally resolved years of return of stratospheric ozone to 1960 levels is presented. Results from the 180-member ensemble, simulated with this newly developed simple climate model, suggest that the spread in return years of ozone is largest around 40_ N/S and in the southern high latitudes and decreases with increasing greenhouse gas emissions. The spread in projections of ozone is larger for higher greenhouse gas scenarios and is larger in the polar regions than in the mid-latitudes, while the spread in ozone radiative forcing is smallest in the polar regions.