Sea level rise in my ‘hood

Sea level rise at my place predicted by ‘coastadapt‘ with a low emissions scenario.    There are a number of components – ocean expansion with warmth, balances between ice and water and groundwater and surface water.  The balances – in the myth that these can be determined realistically – add up to some 0.4m.  My place is set into a granite hill some 15m above mean sea level – which I can see over the mangroves.  It is secure against flooding, fire, storm surge and anything but the largest tsunamis.   For which – as a good planning engineer and environmental scientist over decades – I have escape plans.  Sea level rise seems not a major concern – unless it shifts rapidly for reasons that we are beginning to understand.

https://coastadapt.com.au/sea-level-rise-information-all-australian-coastal-councils#QLD_LIVINGSTONE

As an aside – how realistic is a low emissions scenario?   The technology costings show that renewables are competitive in many places and applications – and getting cheaper.  Small modular reactors (SMR) may well be cost competitive after the first subsidized – and no apology – units roll off assembly lines.

e.g. http://innovationreform.org/wp-content/uploads/2017/07/Advanced-Nuclear-Reactors-Cost-Study.pdf

The first SMRs are expected to be within the range of natural gas plants costs assuming appropriate private-public partnerships to help reduce technology risks and keep first-of-a-kind costs low. The partnerships incentivize the initial SMR customers by addressing typical first-of-a-kind challenges that create unique regulatory, technology and financial risks that translate into higher costs that most companies are unable or unwilling to accept. The partnerships reduce the barriers to technology adoption and allow the learning curve to bring down the cost of future SMRs.

By 2030, after the first few plants begin operation, SMRs would be cost-competitive without further private-public partnerships. For most scenarios, the costs of SMRs are within the range of natural gas plants, such that a utility could choose an SMR based on factors such as long-term price stability and fuel diversity. *  smart nuclear engines.

And much more can be done to reverse C02 losses from soils and ecosystems.

Sea level rise – even with low emissions – is fundamentally unpredictable using temporally chaotic climate models.  The spatio/temporal dynamics of the Earth flow field ensures – as well – that real climate is naturally a moving target.

“Sensitive dependence and structural instability are humbling twin properties for chaotic dynamical systems, indicating limits about which kinds of questions are theoretically answerable. They echo other famous limitations on scientist’s expectations, namely the undecidability of some propositions within axiomatic mathematical systems (Gödel’s theorem) and the uncomputability of some algorithms due to excessive size of the calculation.”  James McWilliams.

The graph below shows the components of sea level rise and how they ‘added up’ over recent decades.  The so called ‘acceleration’ is largely due to Greenland melt.  Cheng et al 2017

https://www.nature.com/nclimate/journal/v7/n7/full/nclimate3325.html

I haven’t checked the paper in detail – but it seems to find that the oceans are warming.  This I plotted using the Global Marine Argo Atlas.  Argo has nearly 4000 floats in areas of oceans ice free and deeper than 2000m.  There is a  large annual variation due to differences in hemispheric land areas.  Oceans warm in the southern hemisphere summer – and warms less – with less ocean exposed to the sun – in the northern hemisphere summer.  Over such a short time of record – natural annual and inter-annual variability is pronounced.   The idea that oceans steadily heat – and thus that with thermal inertia this leads to an energy imbalance – is wrong.              lin7

Over long enough all climate series look like Nile River flows for immutable physical reasons. This shows baseflow – dry season flow – in the Nile River. It is a measure of moisture retained in the landscape. I’m not even positive that the units are cubits – about half a metre.

nile river flow

https://datamarket.com/data/set/22yh/annual-minimum-level-of-nile-river-622-1921#!ds=22yh&display=line

Joseph told Pharaoh that his dreams came from God telling him to prepare for seven years of plenty followed by seven years of famine. The task of Pharaoh was to find a wise and honest man to put some of the abundance of the years of plenty away to provide for the years of need and avert a terrible tragedy.

Because of the importance of Nile River flows to the Egyptian civilisation water levels have been measured for 5,000 years and recorded for more than 1,300. The ‘Nilometer’ – known as al-Miqyas in Arabic – in Cairo dates back to the Arab conquest of Egypt. The Cairo Nilometer has an inner stilling well connected to the river and a central stone pillar on which levels were observed.

Rainfall in the Mediterranean Basin is influenced by ocean surface temperatures in the eastern and central Pacific and the north Atlantic. The variability in ocean surface temperature year to year, decade to decade, century to century result in persistent regimes of droughts and floods at many scales and with irregular beats.

D. Kondrashov and colleagues collated a record of Nile River flood water levels over the same period as te baseflow record.  They calculated the mean of high water levels at 18 cubits. Water levels varied from ‘hunger’ at 12 cubits through abundance at 16 cubits and to disaster at 18 cubits.  This suggests that life in ancient Egypt might best be described as lived on the edge. Perhaps not surprising given Joseph’s source of information – is that they found a strong 7 year signal in the data. The record shows increasing water levels over the past millennia and a prominent spike towards the end. There were signals of 56-year regimes, “a quasi-quadriennial (4.2-year) and a quasi-biennial (2.2-year) mode, as well as additional periodicities of 64, 19, 12, and, most strikingly, 7 years. of variability”.

The richness of climate data behaviour, ‘decade by decade and century by century, testifies to the fundamentally chaotic nature of the system that we are attempting to predict.  It challenges the way in which we evaluate models and emphasizes the importance of continuing to focus on observing and understanding processes and phenomena in the climate system. It is also a classic demonstration of the need for ensemble prediction systems on all time scales in order to sample the range of possible outcomes that even the real world could produce. Nothing is certain.”  Slingo and Palmer 2011   Here they refer to perturbed physics ensembles with a focus on seasonal to decadal simulation.

 

 

 

 

 

 

 

 

 

Advertisements
This entry was posted in Uncategorized. Bookmark the permalink.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s