Feeds:
Posts
Comments

I probably should have started a separate series on rainfall and then woven the results back into the Impacts series. It might take a few articles working through the underlying physics and how models and observations of current and past climate compare before being able to consider impacts.

There are a number of different ways to look at rainfall models and reality:

  • What underlying physics provides definite constraints regardless of individual models, groups of models or parameterizations?
  • How well do models represent the geographical distribution of rain over a climatological period like 30 years? (e.g. figure 8 in Impacts XI – Rainfall 1)
  • How well do models represent the time series changes of rainfall?
  • How well do models represent the land vs ocean? (when we think about impacts, rainfall over land is what we care about)
  • How well do models represent the distribution of rainfall and the changing distribution of rainfall, from lightest to heaviest?

In this article I thought I would highlight a set of conclusions from one paper among many. It’s a good starting point. The paper is A canonical response of precipitation characteristics to global warming from CMIP5 models by Lau and his colleagues, and is freely available, and as always I recommend people read the whole paper, along with the supporting information that is also available via the link.

As an introduction, the underlying physics perhaps provides some constraints. This is strongly believed in the modeling community. The constraint is a simple one – if we warm the ocean by 1K (= 1ºC) then the amount of water vapor above the ocean surface increases by about 7%. So we expect a warmer world to have more water vapor – at least in the boundary layer (typically 1km) and over the ocean. If we have more water vapor then we expect more rainfall. But GCMs and also simple models suggest a lower value, like 2-3% per K, not 7%/K. We will come back to why in another article.

It also seems from models that with global warming, rainfall increases more in regions and times of already high rainfall and reduces in regions and times of low rainfall – the “wet get wetter and the dry get drier”. (Also a marketing mantra that introducing a catchy slogan ensures better progress of an idea). So we also expect changes in the distribution of rainfall. One reason for this is a change in the tropical circulation. All to be covered later, so onto the paper..

We analyze the outputs of 14 CMIP5 models based on a 140 year experiment with a prescribed 1% per year increase in CO2 emission. This rate of CO2 increase is comparable to that prescribed for the RCP8.5, a relatively conservative business-as-usual scenario, except the latter includes also changes in other GHG and aerosols, besides CO2.

A 27-year period at the beginning of the integration is used as the control to compute rainfall and temperature statistics, and to compare with climatology (1979–2005) of rainfall data from the Global Precipitation Climatology Project (GPCP). Two similar 27-year periods in the experiment that correspond approximately to a doubling of CO2 emissions (DCO2) and a tripling of CO2 emissions (TCO2) compared to the control are chosen respectively to compute the same statistics..

Just a note that I disagree with the claim that RCP8.5 is a “relatively conservative business as usual scenario” (see Impacts – II – GHG Emissions Projections: SRES and RCP), but that’s just an opinion, as are all views about where the world will be in population, GDP and cumulative emissions 100-150 years from now. It doesn’t detract from the rainfall analysis in the paper.

For people wondering “what is CMIP5?” – this is the model inter-comparison project for the most recent IPCC report (AR5) where many models have to address the same questions so they can be compared.

Here we see (and along with other graphs you can click to enlarge) what the models show in temperature (top left), mean global rainfall (top right), zonal rainfall anomaly by latitude (bottom left) and the control vs the tripled CO2 comparison (bottom right). The many different colors in the first three graphs are each model, while the black line is the mean of the models (“ensemble mean”). The bottom right graph helps put the changes shown in the bottom left into a perspective – with the different between the red and the blue being the difference between tripling CO2 and today:

From Lau et al 2013

Figure 1 – Click to enlarge

In the figure above, the bottom left graph shows anomalies. We see one of the characteristics of models as a result of more GHGs – wetter tropics and drier sub-tropics, along with wetter conditions at higher latitudes.

From the supplementary material, below we see a better regional breakdown of fig 1d (bottom right in the figure above). I’ll highlight the bottom left graph (c) for the African region. Over the continent, the differences between present day and tripling CO2 seem minor as far as model predictions go for mean rainfall:

From Lau et al 2013

Figure 2 – Click to enlarge

The supplementary material also has a comparison between models and observations. The first graph below is what we are looking at (the second graph we will consider afterwards). TRMM (Tropical Rainfall Measuring Mission) is satellite data and GPCP one rainfall climatology that we met in the last article – so they are both observational datasets. We see that the models over-estimate tropic rainfall, especially south of the equator:

From Lau et al 2013

Figure 3 – Click to enlarge

Rainfall Distribution from Light through to Heavy Rain

Lau and his colleagues then look at rainfall distribution in terms of light rainfall through to heavier rainfall. So, take global rainfall and divide it into frequency of occurrence, with light rainfall to the left and heavy rainfall to the right. Take a look back at the bottom graph in the figure above (figure 3, their figure S1). Note that the horizontal axis is logarithmic, with a ratio of over 1000 from left to right.

It isn’t an immediately intuitive graph. Basically there are two sets of graphs. The left “cluster” is how often that rainfall amount occurred, and the black line is GPCP observations. The “right cluster” is how much rainfall fell (as a percentage of total rainfall) for that rainfall amount and again black is observations.

So lighter rainfall, like 1mm/day and below accounts for 50% of time, but being light rainfall accounts for less than 10% of total rainfall.

To facilitate discussion regarding rainfall characteristics in this work, we define, based on the ensemble model PDF, three major rain types: light rain (LR), moderate rain (MR), and heavy rain (HR) respectively as those with monthly mean rain rate below the 20th percentile (<0.3 mm/day), between response (TCO2 minus control, black) and the inter-model 1s the 40th–70th percentile (0.9–2.4mm/day), and above the 98.5% percentile (>9mm/day). An extremely heavy rain (EHR) type defined at the 99.9th percentile (>24 mm day1) will also be referred to, as appropriate.

Here is a geographical breakdown of the total and then the rainfall in these three categories, model mean on the left and observations on the right:

From Lau et al 2013

Figure 4 – Click to enlarge

We can see that the models tend to overestimate the heavy rain and underestimate the light rain. These graphics are excellent because they help us to see the geographical distribution.

Now in the graphs below we see at the top the changes in frequency of mean precipitation (60S-60N) as a function of rain rate; and at the bottom we see the % change in rainfall per K of temperature change, again as a function of rain rate. Note that the bottom graph also has a logarithmic scale for the % change, so as you move up each grid square the value is doubled.

The different models are also helpfully indicated so the spread can be seen:

From Lau et al 2013

Figure 5 – Click to enlarge

Notice that the models are all predicting quite a high % change in rainfall per K for the heaviest rain – something around 50%. In contrast the light rainfall is expected to be up a few % per K and the medium rainfall is expected to be down a few % per K.

Globally, rainfall increases by 4.5%, with a sensitivity (dP/P/dT) of 1.4% per K

Here is a table from their supplementary material with a zonal breakdown of changes in mean rainfall (so not divided into heavy, light etc). For the non-maths people the first row, dP/P is just the % change in precipitation (“d” in front of a variable means “change in that variable”), the second row is change in temperature and the third row is the % change in rainfall per K (or ºC) of warming from GHGs:

From Lau et al 2013

Figure 6 – Click to enlarge

Here are the projected geographical distributions of the changes in mean (top left), heavy (top right), medium (bottom left) and light rain (bottom right) – using their earlier definitions – under tripling CO2:

From Lau et al 2013

Figure 7 – Click to enlarge

And as a result of these projections, the authors also show the number of dry months and the projected changes in number of dry months:

From Lau et al 2013

Figure 8 – Click to enlarge

The authors conclude:

The IPCC CMIP5 models project a robust, canonical global response of rainfall characteristics to CO2 warming, featuring an increase in heavy rain, a reduction in moderate rain, and an increase in light rain occurrence and amount globally.

For a scenario of 1% CO2 increase per year, the model ensemble mean projects at the time of approximately tripling of the CO2 emissions, the probability of occurring of extremely heavy rain (monthly mean >24mm/day) will increase globally by 100%–250%, moderate rain will decrease by 5%–10% and light rain will increase by 10%–15%.

The increase in heavy rain is most pronounced in the equatorial central Pacific and the Asian monsoon regions. Moderate rain is reduced over extensive oceanic regions in the subtropics and extratropics, but increased over the extratropical land regions of North America, and Eurasia, and extratropical Southern Oceans. Light rain is mostly found to be inversely related to moderate rain locally, and with heavy rain in the central Pacific.

The model ensemble also projects a significant global increase up to 16% more frequent in the occurrences of dry months (drought conditions), mostly over the subtropics as well as marginal convective zone in equatorial land regions, reflecting an expansion of the desert and arid zones..

 

..Hence, the canonical global rainfall response to CO2 warming captured in the CMIP5 model projection suggests a global scale readjustment involving changes in circulation and rainfall characteristics, including possible teleconnection of extremely heavy rain and droughts separated by far distances. This adjustment is strongly constrained geographically by climatological rainfall pattern, and most likely by the GHG warming induced sea surface temperature anomalies with unstable moister and warmer regions in the deep tropics getting more heavy rain, at the expense of nearby marginal convective zones in the tropics and stable dry zones in the subtropics.

Our results are generally consistent with so-called “the rich-getting-richer, poor-getting-poorer” paradigm for precipitation response under global warming..

Conclusion

This article has basically presented the results of one paper, which demonstrates consistency in model response of rainfall to doubling and tripling of CO2 in the atmosphere. In subsequent articles we will look at the underlying physics constraints, at time-series over recent decades and try to make some kind of assessment.

Articles in this Series

Impacts – I – Introduction

Impacts – II – GHG Emissions Projections: SRES and RCP

Impacts – III – Population in 2100

Impacts – IV – Temperature Projections and Probabilities

Impacts – V – Climate change is already causing worsening storms, floods and droughts

Impacts – VI – Sea Level Rise 1

Impacts – VII – Sea Level 2 – Uncertainty

Impacts – VIII – Sea level 3 – USA

Impacts – IX – Sea Level 4 – Sinking Megacities

Impacts – X – Sea Level Rise 5 – Bangladesh

Impacts XI – Rainfall 1

References

A canonical response of precipitation characteristics to global warming from CMIP5 models, William K.-M. Lau, H.-T. Wu, & K.-M. Kim, GRL (2013) – free paper

Further Reading

Here are a bunch of papers that I found useful for readers who want to dig into the subject. Most of them are available for free via Google Scholar, but one of the most helpful to me (first in the list) was Allen & Ingram 2002 and the only way I could access it was to pay $4 to rent it for a couple of days.

Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the hydrologic cycle. Nature 419:224–232

Allan RP (2006) Variability in clear-sky longwave radiative cooling of the atmosphere. J Geophys Res 111:D22, 105

Allan, R. P., B. J. Soden, V. O. John, W. Ingram, and P. Good (2010), Current changes in tropical precipitation, Environ. Res. Lett., doi:10.1088/ 1748-9326/5/52/025205

Physically Consistent Responses of the Global Atmospheric Hydrological Cycle in Models and Observations, Richard P. Allan et al, Surv Geophys (2014)

Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Clim 19:5686–5699

Changes in temperature and precipitation extremes in the CMIP5 ensemble, VV Kharin et al, Climatic Change (2013)

Energetic Constraints on Precipitation Under Climate Change, Paul A. O’Gorman et al, Surv Geophys (2012) 33:585–608

Trenberth, K. E. (2011), Changes in precipitation with climate change, Clim. Res., 47, 123–138, doi:10.3354/cr00953

Zahn M, Allan RP (2011) Changes in water vapor transports of the ascending branch of the tropical circulation. J Geophys Res 116:D18111

If we want to assess forecasts of floods, droughts and crop yields then we will need to know rainfall. We will also need to know temperature of course.

The forte of climate models is temperature. Rainfall is more problematic.

Before we get to model predictions about the future we need to review observations and the ability of models to reproduce them. Observations are also problematic – rainfall varies locally and over short durations. And historically we lacked effective observation systems in many locations and regions of the world, so data has to be pieced together and estimated from reanalysis.

Smith and his colleagues created a new rainfall dataset. Here is a comment from their 2012 paper:

Although many land regions have long precipitation records from gauges, there are spatial gaps in the sampling for undeveloped regions, areas with low populations, and over oceans. Since 1979 satellite data have been used to fill in those sampling gaps. Over longer periods gaps can only be filled using reconstructions or reanalyses..

Here are two views of the global precipitation data from a dataset which starts with the satellite era, that is, 1979 onwards – GPCP (Global Precipitation Climatology Project):

From Adler et al 2003

Figure 1

From Adler et al 2003

Figure 2

For historical data before satellites we only have rain gauge data. The GPCC dataset, explained in Becker et al 2013, shows the number of stations over time by region:

From Becker et al 2013

Figure 3- Click to expand

And the geographical distribution of rain gauge stations at different times:

From Becker et al 2013

Figure 4 – Click to expand

The IPCC compared the global trends over land from four different datasets over the last century and the last half-century:

From IPCC AR5 Ch. 2

Figure 5 – Click to expand

And the regional trends:

From IPCC AR5 Ch. 2

Figure 6 – Click to expand

The graphs for the annual change in rainfall, note the different scales for each region (as we would expect given the difference in average rainfall in different region):

From IPCC AR5 ch 2

Figure 7

We see that the decadal or half-decadal variation is much greater than any apparent long term trend. The trend data (as reviewed by the IPCC in figs 5 & 6) shows significant differences in the datasets but when we compare the time series it appears that the datasets match up better than indicated by the trend comparisons.

The data with the best historical coverage is 30ºN – 60ºN and the trend values for 1951-2000 (from different reconstructions) range from an annual increase of 1 to 1.5 mm/yr per decade (fig 6 / table 2.10 of IPCC report). This is against an absolute value of about 1000 mm/yr in this region (reading off the climatology in figure 2).

This is just me trying to put the trend data in perspective.

Models

Here is the IPCC AR5 chapter 9 on model comparisons to satellite-era rainfall observations. Top left is observations (basically the same dataset as figure 1 in this article over a slightly longer period with different colors) and bottom right is percentage error of model average with respect to observations:

From IPCC AR5 ch 9

Figure 8 – Click to expand

We can see that the average of all models has substantial errors on mean rainfall.

Articles in this Series

Impacts – I – Introduction

Impacts – II – GHG Emissions Projections: SRES and RCP

Impacts – III – Population in 2100

Impacts – IV – Temperature Projections and Probabilities

Impacts – V – Climate change is already causing worsening storms, floods and droughts

Impacts – VI – Sea Level Rise 1

Impacts – VII – Sea Level 2 – Uncertainty

Impacts – VIII – Sea level 3 – USA

Impacts – IX – Sea Level 4 – Sinking Megacities

Impacts – X – Sea Level Rise 5 – Bangladesh

References

IPCC AR5 Chapter 2

Improved Reconstruction of Global Precipitation since 1900, Smith, Arken, Ren & Shen, Journal of Atmospheric and Oceanic Technology (2012)

The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present), Adler et al, Journal of Hydrometeorology (2003)

A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present, A Becker, Earth Syst. Sci. Data (2013)

FitzGerald et al 2008:

Sea-level rise (SLR) poses a particularly ominous threat because 10% of the world’s population (634 million people) lives in low-lying coastal regions within 10 m elevation of sea level (McGranahan et al. 2007). Much of this population resides in portions of 17 of the world’s 30 largest cities, including Mumbai, India; Shanghai, China; Jakarta, Indonesia; Bangkok, Thailand; London; and New York.

In the last article – Sinking Megacities – we saw that some of these cities are sinking due to ground water depletion. To those megacities, this is a much more serious threat than global sea level rise (probably why we see so many marches and protests about ground water depletion).

The paper continues:

..The potential loss of life in low-lying areas is even more graphically illustrated by the 1970 Bhola cyclone that traveled northward through the Bay of Bengal producing a 12-m-high wall of water that drowned a half million people in East Pakistan (now Bangladesh) (Garrison 2005).

In Bangladesh, storms and cyclones are much more of a threat than sea level rise. Here is Karim and Mimura (2008) listing the serious cyclones over the last 60 years:

From Karim and Mimura 2008

Figure 1 – Click to expand

There is an interesting World Bank Report from 2011. First on floods:

In an average year, nearly one quarter of Bangladesh is inundated, with more than three-fifths of land area at risk of floods of varying intensity (Ahmed and Mirza 2000). Every four or five years, a severe flood occurs during the monsoon season, submerging more than three-fifths of the land..

The most recent exceptional flood, which occurred in 2007, inundated 62,300 km² or 42 percent of total land area, causing 1,110 deaths and affecting 14 million people; 2.1 million ha of standing crop land were submerged, 85,000 houses completely destroyed, and 31,533 km of roads damaged. Estimated asset losses from this one event totaled US$1.1 billion (BWDB 2007).

Flooding in Bangladesh results from a complex set of factors, key among which are extremely low and flat topography, uncertain transboundary flow, heavy monsoon rainfall, and high vulnerability to tidal waves and congested drainage channels. Two-thirds of Bangladesh’s land area is less than 5 m above sea level. Each year, an average flow of 1,350 billion m³ of water from the GBM [Ganges, Brahmaputra, and Meghna] basin drains through the country.

From World Bank 2011

Figure 2

I recommend this World Bank report, very interesting, and you can see some idea of the costs of mitigating against floods. These problems are already present – floods are a regular occurrence, some mitigation has already taken place, and more mitigation continues.

I read the entire report and all I could find was that rising sea levels would exacerbate the problems already faced from storm surges: p.6:

Increase in ocean surface temperature and rising sea levels are likely to intensify cyclonic storm surges and further increase the depth and extent of storm surge induced coastal inundation.

However, the projections indicate that sea level rise is much less of a problem compared with possible increases in future storm surges and possible increases in future flooding. And compared with current storm surges and current flooding. We will look at floods and storm surges in future articles.

In the report it’s clear that floods and storms are already major problems. Sea level is harder to analyze. Trying to account for a sea level rise of 0.3m by 2050 when severe storm surges are already 5-10m is not going to make much of a difference. If we had accurate prediction of storm surges, to +/- 0.3m, then sea level rise of 0.3m should definitely be accounted for. But we don’t have anything like that kind of accuracy.

Well, they do some calculations of adaption against storm surges for projected changes up to 2050:

Under the baseline scenario, the adaptation costs total $2.46 billion. In a changing climate, the additional adaptation cost totals US$892 million.

In essence the question is “what is the storm surge for a once in a 10 year storm in 2050”? (I’m sure Bangladesh would really prefer to build protection against a once in 100 year storm). An extra $1bn for future problems, or a total of $3.5bn to cover existing and future problems, seems like money that would be very well spent, representing excellent value.

Nicholls and Cazenave (2010), in relation to the susceptible coastline of Asia and Africa, comment on adaption:

Many impact studies do not consider adaptation, and hence determine worst-case impacts. Yet, the history of the human relationship with the coast is one of an increasing capacity to adapt to adverse change. In addition, the world’s populated coasts became increasingly managed and engineered over the 20th century. The subsiding cities discussed above all remain protected to date, despite large relative SLR.

Analysis based on benefit-cost methods show that protection would be widespread as well-populated coastal areas have a high value and actual impacts would only be a small fraction of the potential impacts, even assuming high-SLR (>1 m/century) scenarios. This suggests that the common assumption of a widespread forced retreat from the shore in the face of SLR is not inevitable. In many densely populated coastal areas, communities advanced the coast seaward via land claim owing to the high value of land (e.g., Singapore).

Yet, protection often attracts new development in low lying areas, which may not be desirable, and coastal defense failures have occurred, such as New Orleans in 2005. Hence, we must choose between protection, accommodation, and planned retreat adaptation options. This choice is both technical and sociopolitical, addressing which measures are desirable, affordable, and sustainable in the long term. Adaptation remains a major uncertainty concerning the actual impacts of SLR.

In the World Bank 2011 report, in chapter 4, after their analysis on risks and costs of storm-induced inundations in 2050 resulting from projected higher cyclonic wind speeds and a projected increase in sea level of 0.27m, they comment, p.24:

As a cautionary note, it should be noted that this analysis did not address the out-migration from coastal zones that a rise in sea level and intensified cyclonic storm surges might induce.

In fact the cost data assumes population growth in the vulnerable regions.

Likewise, here is Hinkel et al (2014):

Coastal flood damages are expected to increase significantly during the 21st century as sea levels rise and socioeconomic development increases the number of people and value of assets in the coastal floodplain.

[Emphasis added].

This assumption bias creates an interpretation challenge. It would be useful to see notes to the effect: “If the population migrates away from this area due to the higher risk, instead the cost will be $X assuming a reduction of Y% in population in this region by 2050“. This extra item of data would create a useful contrast and I’m guessing that we would see impact assessments reduce by a factor of 5 or 10.

It is difficult to see realistic global sea level changes, even to the end of the century, having a big impact on Bangladesh compared with their current problems of annual flooding and frequent large storm surges. Of course, adding an extra 0.5m to the sea level doesn’t improve the situation, but it is an order of magnitude smaller than storm surges.

The adaption costs estimated by the World Bank to protect against storm surges (already required but at least a work in progress) seem moderate in value.

Lastly, I wasn’t able to find a detailed elevation map (with, say, 0.5m resolution), instead the ones I found graded the elevation with respect to sea level in fairly coarse steps. I’m sure the information exists but may be proprietary (in GIS data for example):

Figure 2 – Click to expand

I have to admit that I believed something like 25% of the Bangladesh population were around 1.0m or less above current sea level. This map says that the 0-3m area is quite small. If anyone does have a better resolution map I will post it up.

Articles in this Series

Impacts – I – Introduction

Impacts – II – GHG Emissions Projections: SRES and RCP

Impacts – III – Population in 2100

Impacts – IV – Temperature Projections and Probabilities

Impacts – V – Climate change is already causing worsening storms, floods and droughts

Impacts – VI – Sea Level Rise 1

Impacts – VII – Sea Level 2 – Uncertainty

Impacts – VIII – Sea level 3 – USA

Impacts – IX – Sea Level 4 – Sinking Megacities

References

Coastal Impacts Due to Sea-Level Rise, Duncan M. FitzGerald et al, Annual Rev. Earth Planet. Sci. (2008)

Impacts of climate change and sea-level rise on cyclonic storm surge floods in Bangladesh, Mohammed Fazlul Karim & Nobuo Mimura, Global Environmental Change (2008) – free paper

The Cost of Adapting to Extreme Weather Events in a Changing Climate – Bangladesh, World Bank (2011) – free report

Sea-Level Rise and Its Impact on Coastal Zones, Robert J Nicholls & Anny Cazenave, Science (2010) – free paper

Coastal flood damage and adaptation costs under 21st century sea-level rise, Jochen Hinkel et al, PNAS (2014) – free paper

In Impacts – VIII – Sea level 3 – USA I suggested this conclusion:

So the cost of sea level rise for 2100 in the US seems to be a close to zero cost problem.

Probably the provocative way I wrote the conclusion confused some people. I should have said that it was a very expensive problem. But that it wasn’t a problem that society should pay for, given that anyone moving to the coast since 2005 at the latest would have known that future sea level was considered to be a major problem. By 2100 the youngest people still living right on the sea front, who bought property there before 2005, would be at least 115 years old.

The idea is that “externalities” as economists call them should be paid by the creators of the problem, not the people that incur the problem. In this case, the “victims” are people who ignored the evidence and moved to the coast anyway. Are they still victims? That was my point.

Well, what about outside the US?

Some mega cities have huge problems. Here is Nicholls 2011:

Coastal areas constitute important habitats, and they contain a large and growing population, much of it located in economic centers such as London, New York, Tokyo, Shanghai, Mumbai, and Lagos. The range of coastal hazards includes climate-induced sea level rise, a long-term threat that demands broad response.

Global sea levels rose 17 cm through the twentieth century, and are likely to rise more rapidly through the twenty-first century when a rise of more than 1 m is possible.

In some locations, these changes may be exacerbated by

(1) increases in storminess due to climate change, although this scenario is less certain
(2) widespread human-induced subsidence due to ground fluid withdrawal from, and drainage of, susceptible soils, especially in deltas.

Subsidence?

Over the twentieth century, the parts of Tokyo and Osaka built on deltaic areas subsided up to 5 m and 3 m, respectively, a large part of Shanghai subsided up to 3 m, and Bangkok subsided up to 2 m.

This human-induced subsidence can be mitigated by stopping shallow, subsurface fluid withdrawals and managing water levels, but natural “background” rates of subsidence will continue, and RSLR will still exceed global trends in these areas. A combination of policies to mitigate subsidence has been instituted in the four delta cities mentioned above, combined with improved flood defenses and pumped drainage systems designed to avoid submergence and/ or frequent flooding.

In contrast, Jakarta and Metro Manila are subsiding significantly, with maximum subsidence of 4 m and 1 m to date, respectively (e.g., Rodolfo and Siringan, 2006; Ward et al., 2011), but little systematic policy response is in place in either city, and future flooding problems are anticipated.

Subsidence graphic:

From Nicholls 2011

Figure 1

To put these figures in context, sea level rise from 1900-2000 was about 0.2m and according to the latest IPCC report the forecast of sea level rise by 2100 might be around an additional 0.5m (for RCP 6.0, see earlier article). In the light of the idea that global society should pay for problems to people caused by global society, perhaps the problems of Shanghai, Bangkok and other sinking cities are not global problems?

Here is Wang et al from 2012:

Shanghai is low-lying, with an elevation of 3–4 m. A quarter of the area lies below 3 m. The city’s flood-control walls are currently more than 6 m high. However, given the trend of sea level rise and land subsidence, this is inadequate. Shanghai is frequently affected by extreme tropical storm surges. The risk of flooding from overtopping is considerable..

..From 1921 to 1965, the average cumulative subsidence of the city center was 1.76 m, with a maximum of 2.63 m. From 1966 to 1985, a monitoring network was established and subsidence was mitigated through artificial recharge. Land subsidence was stabilized at an average of 0.9 mm/year. As a result of rapid urban development and large-scale construction projects between 1986 and 1997, subsidence of the downtown area increased rapidly, at an average rate of 10.2 mm/year..

..In 2100, sea level rise and land subsidence will be far greater than before. Sea level rise is estimated to be 43 cm, while land subsidence is estimated to be 3–229 cm, and neotectonic subsidence is estimated to be 14 cm. Flooding will be severe in 2100 (Fig. 8).

[Note I changed the data in the last paragraph cited to round numbers in cm from their values quoted to 0.01cm – for example, 43cm instead of the paper’s values of 43.31 etc].

So for Shanghai at least global sea level rise is not really the problem.

Given that I don’t pay much attention to media outlets I probably missed the big Marches against Ground Water Depletion Slightly Accentuating Global Warming’s Sea Level Rise in Threatened Megacities.

As with the USA data the question of increased storm surges accentuating global sea level rise is still on the agenda (i.e., has not yet been discussed).

References

Planning for the impacts of sea level rise, RJ Nicholls, Oceanography (2011)

Evaluation of the combined risk of sea level rise, land subsidence, and storm surges on the coastal areas of Shanghai, China, Jun Wang, Wei Gao, Shiyuan Xu & Lizhong Yu, Climatic Change (2012)

 

[I started writing this some time ago and got side-tracked, initially because aerosol interaction in clouds and rainfall is quite fascinating with lots of current research and then because there are many papers on higher resolution simulations of convection that also looked interesting.. so decided to post it less than complete because it will be some time before I can put together a more complete article..]

In Part Four of this series we looked at the paper by Mauritsen et al (2012). Isaac Held has a very interesting post on his blog – and people interested in understanding climate science will benefit from reading his blog – he has been in the field writing papers for 40 years). He highlighted this paper: Cloud tuning in a coupled climate model: Impact on 20th century warming, Jean-Christophe Golaz, Larry W. Horowitz, and Hiram Levy II, GRL (2013).

Their paper has many similarities to Mauritsen et al (2013). Here are some of their comments:

Climate models incorporate a number of adjustable parameters in their cloud formulations. They arise from uncertainties in cloud processes. These parameters are tuned to achieve a desired radiation balance and to best reproduce the observed climate. A given radiation balance can be achieved by multiple combinations of parameters. We investigate the impact of cloud tuning in the CMIP5 GFDL CM3 coupled climate model by constructing two alternate configurations.

They achieve the desired radiation balance using different, but plausible, combinations of parameters. The present-day climate is nearly indistinguishable among all configurations.

However, the magnitude of the aerosol indirect effects differs by as much as 1.2 W/m², resulting in significantly different temperature evolution over the 20th century..

 

..Uncertainties that arise from interactions between aerosols and clouds have received considerable attention due to their potential to offset a portion of the warming from greenhouse gases. These interactions are usually categorized into first indirect effect (“cloud albedo effect”; Twomey [1974]) and second indirect effect (“cloud lifetime effect”; Albrecht [1989]).

Modeling studies have shown large spreads in the magnitudes of these effects [e.g., Quaas et al., 2009]. CM3 [Donner et al., 2011] is the first Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model to represent indirect effects.

As in other models, the representation in CM3 is fraught with uncertainties. In particular, adjustable cloud parameters used for the purpose of tuning the model radiation can also have a significant impact on aerosol effects [Golaz et al., 2011]. We extend this previous study by specifically investigating the impact that cloud tuning choices in CM3 have on the simulated 20th century warming.

What did they do?

They adjusted the “autoconversion threshold radius”, which controls when water droplets turn into rain.

Autoconversion converts cloud water to rain. The conversion occurs once the mean cloud droplet radius exceeds rthresh. Larger rthresh delays the formation of rain and increases cloudiness.

The default in CM3 was 8.2 􏰃μm. They selected alternate values from other GFDL models: 6.0 􏰃μm (CM3w) and 10.6 􏰃μm (CM3c). Of course, they have to then adjust others parameters to achieve radiation balance – the “erosion time” (lateral mixing effect reducing water in clouds) which they note is poorly constrained (that is, we don’t have some external knowledge of the correct value for this parameter) and the “velocity variance” which affects how quickly water vapor condenses out onto aerosols.

Here is the time evolution in the three models (and also observations):

 

From Golaz et al 2013

From Golaz et al 2013

Figure 1 – Click to enlarge

In terms of present day climatology, the three variants are very close, but in terms of 20th century warming two variants are very different and only CM3w is close to observations.

Here is their conclusion, well worth studying. I reproduce it in full:

CM3w predicts the most realistic 20th century warming. However, this is achieved with a small and less desirable threshold radius of 6.0 􏰃μm for the onset of precipitation.

Conversely, CM3c uses a more desirable value of 10.6 􏰃μm but produces a very unrealistic 20th century temperature evolution. This might indicate the presence of compensating model errors. Recent advances in the use of satellite observations to evaluate warm rain processes [Suzuki et al., 2011; Wang et al., 2012] might help understand the nature of these compensating errors.

CM3 was not explicitly tuned to match the 20th temperature record.

However, our findings indicate that uncertainties in cloud processes permit a large range of solutions for the predicted warming. We do not believe this to be a peculiarity of the CM3 model.

Indeed, numerous previous studies have documented a strong sensitivity of the radiative forcing from aerosol indirect effects to details of warm rain cloud processes [e.g., Rotstayn, 2000; Menon et al., 2002; Posselt and Lohmann, 2009; Wang et al., 2012].

Furthermore, in order to predict a realistic evolution of the 20th century, models must balance radiative forcing and climate sensitivity, resulting in a well-documented inverse correlation between forcing and sensitivity [Schwartz et al., 2007; Kiehl, 2007; Andrews et al., 2012].

This inverse correlation is consistent with an intercomparison-driven model selection process in which “climate models’ ability to simulate the 20th century temperature increase with fidelity has become something of a show-stopper as a model unable to reproduce the 20th century would probably not see publication” [Mauritsen et al., 2012].

Very interesting paper, and freely availableKiehl’s paper, referenced in the conclusion, is also well-worth reading. In his paper he shows that models with the highest sensitivity to GHGs have the highest negative value from 20th century aerosols, while the models with the lowest sensitivity to GHGs have the lowest negative value from 20th century aerosols. Therefore, both ends of the range can reproduce 20th century temperature anomalies, while suggesting very different 21st century temperature evolution.

A paper on higher resolution models, Siefert et al 2015, did some model experiments, “large eddy simulations”, which are much higher resolution than GCMs. The best resolution GCMs today typically have a grid size around 100km x 100km. Their LES model had a grid size of 25m x 25m, with 2048 x 2048 x 200 grid points, to span a simulated volume of 51.2 km x 51.2 km x 5 km, and ran for a simulated 60hr time span.

They had this to say about the aerosol indirect effect:

It has also been conjectured that changes in CCN might influence cloud macrostructure. Most prominently, Albrecht [1989] argued that processes which reduce the average size of cloud droplets would retard and reduce the rain formation in clouds, resulting in longer-lived clouds. Longer living clouds would increase cloud cover and reflect even more sunlight, further cooling the atmosphere and surface. This type of aerosol-cloud interaction is often called a lifetime effect. Like the Twomey effect, the idea that smaller particles will form rain less readily is based on sound physical principles.

Given this strong foundation, it is somewhat surprising that empirical evidence for aerosol impacts on cloud macrophysics is so thin.

Twenty-five years after Albrecht’s paper, the observational evidence for a lifetime effect in the marine cloud regimes for which it was postulated is limited and contradictory. Boucher et al. [2013] who assess the current level of our understanding, identify only one study, by Yuan et al. [2011], which provides observational evidence consistent with a lifetime effect. In that study a natural experiment, outgassing of SO2 by the Kilauea volcano is used to study the effect of a changing aerosol environment on cloud macrophysical processes.

But even in this case, the interpretation of the results are not without ambiguity, as precipitation affects both the outgassing aerosol precursors and their lifetime. Observational studies of ship tracks provide another inadvertent experiment within which one could hope to identify lifetime effects [Conover, 1969; Durkee et al., 2000; Hobbs et al., 2000], but in many cases the opposite response of clouds to aerosol perturbations is observed: some observations [Christensen and Stephens, 2011; Chen et al., 2012] are consistent with more efficient mixing of smaller cloud drops leading to more rapid cloud desiccation and shorter lifetimes.

Given the lack of observational evidence for a robust lifetime effect, it seems fair to question the viability of the underlying hypothesis.

In their paper they show a graphic of what their model produced, it’s not the main dish but interesting for the realism:

From Seifert et al 2015

From Seifert et al 2015

Figure 2 – Click to expand

It is an involved paper, but here is one of the conclusions, relevant for the topic at hand:

Our simulations suggest that parameterizations of cloud-aerosol effects in large-scale models are almost certain to overstate the impact of aerosols on cloud albedo and rain rate. The process understanding on which the parameterizations are based is applicable to isolated clouds in constant environments, but necessarily neglects interactions between clouds, precipitation, and circulations that, as we have shown, tend to buffer much of the impact of aerosol change.

For people new to parameterizations, a couple of articles that might be useful:

Conclusion

Climate models necessarily have some massive oversimplifications, as we can see from the large eddy simulation which has 25m x 25m grid cells while GCMs have 100km x 100km at best. Even LES models have simplifications – to get to direct numerical solution (DNS) of the equations for turbulent flow we would need a scale closer to a few millimeters rather than meters.

The over-simplifications in GCMs require “parameters” which are not actually intrinsic material properties, but are more an average of some part of a climate process over a large scale. (Note that even if we had the resolution for the actual fundamental physics we wouldn’t necessary know the material parameters necessary, especially in the case of aerosols which are heterogeneous in time and space).

As the climate changes will these “parameters” remain constant, or change as the climate changes?

References

Cloud tuning in a coupled climate model: Impact on 20th century warming, Jean-Christophe Golaz, Larry W. Horowitz, and Hiram Levy II, GRL (2013) – free paper

Twentieth century climate model response and climate sensitivity, Jeffrey T. Kiehl, GRL (2007) – free paper

Large-eddy simulation of the transient and near-equilibrium behavior of precipitating shallow convection, Axel Seifert et al, Journal of Advances in Modeling Earth Systems (2015) – free paper

In Parts VI and VII we looked at past and projected sea level rise. It is clear that the sea level has risen over the last hundred years, and it’s clear that with more warming sea level will rise some more. The uncertainties (given a specific global temperature increase) are more around how much more ice will melt than how much the ocean will expand (warmer water expands). Future sea level rise will clearly affect some people in the future, but very differently in different countries and regions. This article considers the US.

A month or two ago, via a link from a blog, I found a paper which revised upwards a current calculation (or average of such calculations) of damage due to sea level rise in 2100 in the US. Unfortunately I can’t find the paper, but essentially the idea was people would continue moving to the ocean in ever increasing numbers, and combined with possible 1m+ sea level rise (see Part VI & VII) the cost in the US would be around $1TR (I can’t remember the details but my memory tells me this paper concluded costs were 3x previous calculations due to this ever increasing population move to coastal areas – in any case, the exact numbers aren’t important).

Two examples that I could find (on global movement of people rather than just in the US), Nicholls 2011:

..This threatened population is growing significantly (McGranahan et al., 2007), and it will almost certainly increase in the coming decades, especially if the strong tendency for coastward migration continues..

And Anthoff et al 2010

Fifthly, building on the fourth point, FUND assumes that the pattern of coastal development persists and attracts future development. However, major disasters such as the landfall of hurricanes could trigger coastal abandonment, and hence have a profound influence on society’s future choices concerning coastal protection as the pattern of coastal occupancy might change radically.

A cycle of decline in some coastal areas is not inconceivable, especially in future worlds where capital is highly mobile and collective action is weaker. As the issue of sea-level rise is so widely known, disinvestment from coastal areas may even be triggered without disasters..

I was struck by the “trillion dollar problem” paper and the general issues highlighted in other papers. The future cost of sea level rise in the US is not just bad, it’s extremely expensive because people will keep moving to the ocean.

Why are people moving to the coast?

So here is an obvious take on the subject that doesn’t need an IAM (integrated assessment model).. Perhaps lots of people missed the IPCC TAR (third assessment report) in 2001. Perhaps anthropogenic global warming fears had not reached a lot of the population. Maybe it didn’t get a lot of media coverage. But surely no could have missed Al Gore’s movie. I mean, I missed it from choice, but how could anyone in rich countries not know about the discussion?

So anyone since 2006 (arbitrary line in the sand) who bought a house that is susceptible to sea level rise is responsible for their own loss that they incur around 2100. That is, if the worst fears about sea level rise play out, combined with more extreme storms (subject of a future article) which create larger ocean storm surges, their house won’t be worth much in 2100.

Now, barring large increases in life expectancy, anyone who bought a house in 2005 will almost certainly be dead in 2100. There will be a few unlucky centenarians.

Think of it as an estate tax. People who have expensive ocean-front houses will pass on their now worthless house to their children or grandchildren. Some people love the idea of estate taxes – in that case you have a positive. Some people hate the idea of estate taxes – in that case strike it up as a negative. And, drawing a long bow here, I suspect a positive correlation between concern about climate change and belief in the positive nature of estate taxes, so possibly it’s a win-win for many people.

Now onto infrastructure.

From time to time I’ve had to look at depreciation and official asset life for different kinds of infrastructure and I can’t remember seeing one for 100 years. 50 years maybe for civil structures. I’m definitely not an expert. That said, even if the “official depreciation” gives something a life of 50 years, much is still being used 150 years later – buildings, railways, and so on.

So some infrastructure very close to the ocean might have to be abandoned. But it will have had 100 years of useful life and that is pretty good in public accounting terms.

Why is anyone building housing, roads, power stations, public buildings, railways and airports in the US in locations that will possibly be affected by sea level rise in 2100? Maybe no one is.

So the cost of sea level rise for 2100 in the US seems to be a close to zero cost problem.

These days, if a particular area is recognized as a flood plain people are discouraged from building on it and no public infrastructure gets built there. It’s just common sense.

Some parts of New Orleans were already below sea level when Hurricane Katrina hit. Following that disaster, lots of people moved out of New Orleans to a safer suburb. Lots of people stayed. Their problems will surely get worse with a warmer climate and a higher sea level (and also if storms gets stronger – subject of a future article). But they already had a problem. Infrastructure was at or below sea level and sufficient care was not taken of their coastal defences.

A major problem that happens overnight, or over a year, is difficult to deal with. A problem 100 years from now that affects a tiny percentage of the land area of a country, even with a large percentage (relatively speaking) of population living there today, is a minor problem.

Perhaps the costs of recreating current threatened infrastructure a small distance inland are very high, and the existing infrastructure would in fact have lasted more than 100 years. In that case, people who believe Keynesian economics might find the economic stimulus to be a positive. People who don’t think Keynesian economics does anything (no multiplier effect) except increase taxes, or divert productive resources into less productive resources will find it be a negative. Once again, drawing a long bow, I see a correlation between people more concerned about climate change also being more likely to find Keynesian economics a positive. Perhaps again, there is a win-win.

In summary, given the huge length of time to prepare for it, US sea level rise seems like a minor planning inconvenience combined with an estate tax.

Articles in this Series

Impacts – I – Introduction

Impacts – II – GHG Emissions Projections: SRES and RCP

Impacts – III – Population in 2100

Impacts – IV – Temperature Projections and Probabilities

Impacts – V – Climate change is already causing worsening storms, floods and droughts

Impacts – VI – Sea Level Rise 1

Impacts – VII – Sea Level 2 – Uncertainty

References

Planning for the impacts of sea level rise, RJ Nicholls, Oceanography (2011)

The economic impact of substantial sea-level rise, David Anthoff et al, Mitig Adapt Strateg Glob Change (2010)

In Part VI we looked at past and projected sea level rise. There is significant uncertainty in future sea level rise, even assuming we know the future global temperature change. The uncertainty results from “how much ice will melt?”

We can be reasonably sure of sea level rise from thermal expansion (so long as we know the temperature). By contrast, we don’t have much confidence in the contribution from melting ice (on land). This is because ice sheet dynamics (glaciers, Greenland & Antarctic ice sheet) are non-linear and not well understood.

Here’s something surprising. Suppose you live in Virginia near the ocean. And suppose all of the Greenland ice sheet melted in a few years (not possible, but just suppose). How much would sea level change in Virginia? Hint: the entire Greenland ice sheet converted into global mean sea level is about 7m.

Zero change in Virginia.

Here are charts of relative sea level change across the globe for Greenland & West Antarctica, based on a 1mm/yr contribution from each location – click to expand:

From Tamisiea 2011

From Tamisiea 2011

Figure 1 – Click to Expand

We see that the sea level actually drops close to Greenland, stays constant around mid-northern latitudes in the Atlantic and rises in other locations. The reason is simple – the Greenland ice sheet is a local gravitational attractor and is “pulling the ocean up” towards Greenland. Once it is removed, the local sea level drops.

Uncertainty

If we knew for sure that the global mean temperature in 2100 would be +2ºC or +3ºC compared to today we would have a good idea in each case of the sea level rise from thermal expansion. But not much certainty on any rise from melting ice sheets.

Let’s consider someone thinking about the US for planning purposes. If the Greenland ice sheet contributes lots of melting ice, the sea level on the US Atlantic coast won’t be affected at all and the increase on the Pacific coast will be significantly less than the overall sea level rise. In this case, the big uncertainty in the magnitude of sea level rise is not much of a factor for most of the US.

If the West Antarctic ice sheet contributes lots of melting ice, the sea level on the east and west coasts of the US will be affected by more than the global mean sea level rise.

For example, imagine the sea level was expected to rise 0.3m from thermal expansion by 2100. But there is a fear that ice melting will cause 0 – 0.5m global rise. A US policymaker really needs to know which ice sheet will melt. The “we expect at most an additional 0.5m from melting ice” tells her that she might have – in total – a maximum sea level rise of 0.3m on the east coast and a little more than 0.3m on the west coast if Greenland melts; but she instead might have – in total – a maximum of almost 1m on each coast if West Antarctica melts.

The source of the melting ice just magnifies the uncertainty for policy and economics.

If this 10th century legend was still with us maybe it would be different (we only have his tweets):

Donaeld the Unready

Articles in this Series

Impacts – I – Introduction

Impacts – II – GHG Emissions Projections: SRES and RCP

Impacts – III – Population in 2100

Impacts – IV – Temperature Projections and Probabilities

Impacts – V – Climate change is already causing worsening storms, floods and droughts

Impacts – VI – Sea Level Rise 1

References

The moving boundaries of sea level change: Understanding the origins of geographic variability, ME Tamisiea & JX Mitrovica, Oceanography (2011)