Q: What are the key assumptions on which the IPCC SRES scenarios are based? What are the main differences between the A1FI and B2 scenarios? What is the principal difference between the IPCC SRES (pre-2012) and RCP (AR5) scenarios?
Ans:
A1FI: Deals with rapid economic growth in global context and rapid introductions of new and more efficient technologies,with emphasis on fossil fuel intensive,while
B2: Deals with an emphasis on local solutions to economic and environmental sustainability having more environmental and original approaches.
The principal difference between the IPCC SRES (pre-2012) and RCP (AR5) scenarios:
1. The IPCC SRES (pre-2012), scenarios had socioeconomic storylines but climate mitigation options were not included as in case of RCP (AR5) scenario.
2. RCP (AR5) scenario intended to provide a flexible, interactive, and iterative approach to climate change scenarios which were not included in IPCC SRES (pre-2012).
3. In RCP (AR5) scenario, socioeconomic scenarios, emissions, and subsequent radiative forcing pathways were linked to one,which were absent in IPCC SRES (pre-2012) .
4. All RCPs adopted stringent air pollution mitigation policies and thus have much lower tropospheric ozone and aerosol abundances than the SRES scenarios, which ignored the role of air quality regulations.
Q: Why is it important to be able to downscale climate models to spatial scales below about 50 km?Ans: It important to downscale climate models to spatial scales below about 50 km,because Global Climate Models (GCMs) used for climate studies and climate projections, run at coarse spatial resolution (in 2012, typically of the order 50 kilometres (31 mi),and were unable to resolve important sub-grid scale features such as clouds and topography. As a result GCM output can not be used for local impact studies.
To overcome this problem downscaling methods were used to obtain local-scale weather and climate, particularly at the surface level, from regional-scale atmospheric variables that are provided by GCMs.
Q: If you know the physical laws for how a system changes over time, in what ways could this system be unpredictable? What is this relevance of this to climate change modelling?
Ans: If I knew that the physical laws for a system changes over time, then the system could be unpredictable in a number of ways:
1. It's a complex system that intervenes with the physical law having time delays.
2. Over passage of time,there could be non linear interactions within the system.
3. There might be loopholes in the feedback system.
4. Just because the system might be deterministic,it doesn't guarantee its predictability to the future.
The relevance of the above to climate change modelling are:
1. weather can be predicted.
2. Catastrophic shifts in the ecosystems can be predicted over time.
3. Ocean ecosystem can be predicted.
Q: Why can it be helpful to model a system at the level of individual agents instead of only considering population averages in the model? Why wouldn't both approaches give the same answer?
Ans: It can be helpful to model a system at the level of individual agents instead of only considering population averages in the model because,it's a complex system and we often need to use techniques like agent based modelling to understand how system behaves,the ways in which agents are being connected to each other and how this connection would ultimately effect the system behavior.
Both approaches would not give the same answer because Individual agents probability of occurrence in a population set would be different from those of when we consider the population averages.i.e the in case of individual agents the probability of occurrence would be considered that are most likely to occur while in population average,the average probabilities would be considered to determine the result.
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