Globally important cultural and economic features are situated in the coastal and near coastal regions of the world. Assets, such as cities and their related infrastructure, ports, transport and energy corridors, mass and boutique tour¬ism sites and cultural heritage sites are already know to be situated in vulnerable coastal locations. All these and more types of sites will become increasingly vulnerable as sea levels rise. Global circulation models of sea level rise combined with data on vertical land movement offer opportunities for data presentation previously unavailable to such a wide audience. The application of Esri tools and know-how and other third party software has made this possible in this app.
The GCM data used in this App is from CMIP5. For more information on CMIP5 please visit: http://cmip-pcmdi.llnl.gov/cmip5/guide_to_cmip5.html. It is important to note that not all models available in the CMIP5 database contain data on all climate variables.
The monthly changes in SLR of 28 GCMs between 1986-2005 and 2081-2100 under the RCP4.5 scenario were calculated using a pattern scaling method.
GCM data were retrieved from the Earth System Grid (ESG) data portal for CMIP5, including: the sea surface height (‘zos’), the global average thermosteric sea level change (‘zostoga’) and the global average sea level change (’zosga’) under the RCP4.5 scenario. For some GCMs, only ‘zosga’ was available and was therefore used instead of zostoga. The data availability is shown in Table 1. Please find details on GCMs and RCP scenarios below in our 2013 Data Manual, and more information on the variables applied can be found at http://www.climatechange2013.org/images/report/WG1AR5_Ch13SM_FINAL.pdf and http://www-pcmdi.llnl.gov/ipcc/standard_output.pdf.
The change in sea surface height was scaled by the change in global average thermal expansion by month following the pattern scaling theory (please see details in our 2013 Data Manual). The sea surface height from GCMs includes the regional variability of dynamic topography changes due to water mass advection, thermohaline circulation and to the wind-driven circulation, but did not include the tidal effects. The changes in global average thermal expansion were calculated by the changes in zostoga if available, or by the changes in zosga.
The value X (m/m) in the patterns is interpreted as “regional sea level may rise X m when the global average sea level rises 1 m”.
Table 1: Available GCMs with sea level rise variables in CMIP5 global data
The Representative Concentration Pathways (RCPs) are four greenhouse gas concentration (not emissions) trajectories adopted by the IPCC for its Fifth Assessment Report (AR5). The four RCPs, RCP2.6, RCP4.5, RCP6.0, and RCP8.5, are named after a possible range of radiative forcing values in the year 2100 (of 2.6, 4.5, 6.0, and 8.5 W/m2, respectively) (Table 1).
|Description *||CO2 Equivalent||SRES Equivalent||Publication – IA Model|
|RCP8.5||Rising radiative forcing pathway leading to 8.5 W/m2 in 2100.||1370||A1FI||Raiahi et al. 2007 – MESSAGE|
|RCP6.0||Stabilization without overshoot pathway to 6 W/m2 at 2100||850||B2||Fujino et al.; Hijioka et al. 2008 – AIM|
|RCP4.5||Stabilization without overshoot pathway to 4.5 W/m2 2100||650||B1||Clark et al. 2006; Smith and Wigley 2006; Wise et al. 2009 – GCAM|
|RCP2.6||Peak in radiative forcing at ~ 3 W/m2 before 2100 and decline||490||None||van Vuuren et al., 2007; van Vuuren et al. 2006 - IMAGE|
* Approximate radiative forcing levels were defined as ±5% of the stated level in W/m2 relative to pre-industrial levels. Radiative forcing values include the net effect of all anthropogenic GHGs and other forcing agents.
An important element in the process is accounting for variations over a year which can cause higher than average sea levels in some months for certain locations. This seasonal aspect has been taken into account by focusing on the month with the highest current level and comparing this with the month with the highest level in the future. The actual month will be different in different locations.
The App shows a global map of the combined processes of local (absolute) sea level rise and local vertical land movement. The sea level rise values are taken as the median value of an ensemble of 28 GCM’s, under the assumption of the largest greenhouse gas emissions as described by the RCP8.5 scenario in AR5. It also assumes a high climate sensitivity.
Vertical land movement (VLM) is a generic term for all processes that impact the elevation at a given locations (tectonic movements, subsidence, ground water extraction), causing land to move up or down. This is typically a slow process with magnitudes commonly between -10 (sinking) and +10 (rising) mm/year. However, rapid, often very localised and extreme land movements can occur with earthquakes.
Vertical land movement becomes relevant when looking at the local effects of sea level rise. Because the order of magnitude can be comparable, VLM can either exacerbate or dampen the sea level rise experienced at a coastal location. In a place where VLM is upward (rising, like Norway), the local experienced SLR is smaller (local SLR can even be negative: sea level going down). When VLM is downward (sinking, like Manila), local experienced SLR is stronger.
Because of its (potential) magnitude VLM must be considered when sea level rise effects are determined on a local scale.
Note that local sea level rise is likely to be different from the global mean (regardless of VLM), because of variations in currents, the amount of heating of the sea water (responsible for the thermal expansion), as well as the volume (depth) of the sea water affected. This is expressed through change patterns (extracted from GCM-results) representing the ratio (i.e. in cm/cm) of local SLR vs. global mean SLR (the so-called normalized value).
Vertical land movement can be observed directly, or inferred from related measurements.
Direct observations are available through the SONEL initiative (http://www.sonel.org/) whereby VLM is estimated from continuous GPS measurements at fixed locations, often coinciding with tidal observation stations. The latest set of “solutions” (http://www.sonel.org/-GPS-Solutions-.html?lang=en) contains location (lat/lon) and estimates of VLM (mm/year). As there are requirements for determining the trend (length of the period, completeness, quality, stability of the solution), not all stations have an associated value.
The Permanent Service for Mean Sea Level (http://www.psmsl.org/) maintains an archive of observed tides. An analysis of the data for these stations to estimate their trends (which are reflecting the rise in sea level), is also available (http://www.psmsl.org/products/trends/trends.txt). Note that not all stations meet the requirements of completeness, total number of observations and quality of measurements.
The local observed sea level rise and local vertical land movement have the following relation:
local observed SLR = local absolute SLR – local VLM
(with VLM>0 means that land is rising, VLM<0 land is sinking)
local absolute SLR = global SLR (over the observation period) * local normalized value (from an ensemble of GCMs)
To determine the global SLR over the period that the tidal observations were made, the following curve is used.
Caveat: The global curve is based on tidal observation (up to 1992, after 1992, satellite observations are used). In order to do that, assumptions needed to be made about the local VLM at each tidal station. A global model (mostly for tectonic movements) was used to do this. This creates a “loop”: as this paper attempts to estimate local VLM from data that has been corrected with a modelled VLM. The assumption is that the averaging of the data around the globe minimizes this bias.
To be able to use VLM in places where it has not been observed, the VLM values in the (SONEL or PSMSL) point locations needed to be interpolated spatially over a grid (note that only values at coast lines are used). ArcGIS has multiple models for spatial interpolation of point values which were tested on their performance noting:
The conclusion is that the IDW (Inverse Distance Weighted) model is the most useable. The following parameter choices were made:
|output cell size = 0.25°x0.25°||to confirm with resolution of SLR-patterns|
|power = 2.5||dampens the spatial extend of outliers|
|number of points = 12||creates acceptable spatial coherency|
|extend = -180,180,-90,90||global coverage|
Caveat: The description of the IDW tool is unclear on how it deals with coordinate system issues. As the station locations are in LAT/LON, the distances between the stations are not simple equations. It is assumed ArcGIS deals with this issue. If this is not the case, there is a bias in the inverse distance weighing. As most stations are “local” (relatively close to each other), for most calculations this will only result in a potentially small error.
A problem is that the IDW tool does not wrap around the globe (crossing the 360° to 0° meridian). This was managed by executing the IDW tool twice (only for the combined result, see below): once with the 180° meridian centred and once with the 0° meridian centred (the longitudes of the observation stations were either mapped on 0° to 360°, or on -180° to 180°). The resulting images were joined using the following scheme:
The 0° to 360° image was reassembled as: B3+A2+A3+B2, while the -180° to 180°image resulted from A3+B2+B3+A2.
The global picture from the SONEL data looks like (contour-lines in mm/year) (without wrap-around correction):
This shows a few hotspots in the Gulf of Mexico, the Indian Ocean and the Date Line in the Pacific Ocean were land is sinking (red, VLM<0), while the Arctic, and parts of the Antarctic regionare rising (green).
The PSMSL dataset leads to the following image (contour lines in mm/year) (without wrap-around correction):
The “big” picture is the same as for the SONEL data, with some marked differences like the west coast of Mexico, the Indian Ocean and the Antarctic, mostly caused by a lack of data in the Southern Hemisphere.
Combing the two datasets to generate a richer source of information results in the following image (contour lines in mm/year) (with wrap-around correction):
This is considered given the applied datasets the best estimate of combining all available information.
Using the ArcGIS “Raster to ASCII” and the SimCLIM “Image Importer” functions a VLM.img file was created that was used in SimCLIM 2013 in combination with the map-calculator to create spatial images for locally experienced SLR (including VLM).
The methodology described combines multiple techniques and datasets to get a best estimate of local vertical land movement around the globe. Its practical usage is limited to the coastlines, where most data is collected, making use in those regions more reliable. The methodology can be reapplied to update the resulting image when new information from SONEL and/or PSMSL becomes available.
For the Sea Level Rise and Vertical Land Movement app there are a number of options for displaying the outputs. Underlying all the outputs are the fundamental data sets of the RCP 8.5 ensemble of sea level rise patterns in conjunction with the output layer for vertical land movement. The outputs displayed in the app combine these two aspects and present them as a graphic that matches the colour scheme for the larger ocean output. In the case of the site specific outputs the image include data that extends from the IPCC defined baseline of 1995 and includes levels for the years 2002, 2040, 2060, 2080 and 2100. The latitude and longitude is also displayed for the site as well as options for management of the site data. It can be either hidden, deleted or shared (via Facebook). There is also an option for highlighting multiple sites and sharing them. The latitude and longitude are also defined in the url and it can be copied and shared with others i.e. when the url is opened in another bowser it not only opens the App but also the location and its data that is saved in the url.
Sea level rise is a very local affair. Multiple variables interact resulting in considerable variation around the world’s coastlines in how sea level rise is expressed. The values generated through the app represent those values realised through the application of an ensemble of all the CMIP5 general circulation models that have sea level rise patterns. Across the 28 models there are some where the modeled sea level rise for a location will exceed the ensemble mean while others will be less. Those that will use this type of sea level rise data need to be aware of this variability in model outcomes. Moreover, the models used in this app are only used to express potential change in sea level rise out to the year 2100. Where seas are rising they will continue to rise for centuries after the year 2100 given the inertia already in the global climate system set in train by current greenhouse gas concentrations. The app also only expresses the potential sea level rise levels that could be realized given current trends in greenhouse gas emissions that are in line with the representative concentration pathway of 8.5 W/m2 in 2100. The future could be better or worse than this assessment. However the information provided by the app is not the only scenario for the future; there are many that will evolve and therefore this one should not be construed as a prediction of any future level for a given location.
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