Last modified on Jun 20, 2024 14:54

Contributors: R. Wilson (PML), J. Clark (PML)

1. Known issues in model outputs

2. Erroneous time point in April 2047 daily data for RCP 8.5 simulation

In the RCP8.5 daily temperature and salinity fields, there is an erroneous time point in the April data for 2047. Values across the domain are recorded as zeros when this should not be the case. When working with the data, care should be taken (e.g. using masking) to avoid the data point negatively impacting workflows.

3. Model validation

Surface chlorophyll and sea surface temperature output by the NEMO-ERSEM model (Butenschön et al. 2016) for the Northeast Atlantic have been compared to satellite values, using monthly values for years 2000-2015. Satellite chlorophyll data come from the ESA Climate Change Initiative Ocean Colour project (http://www.esa-oceancolour-cci.org/, v4.0). Sea surface temperature is from the OSTIA dataset (Donlon et al. 2012), downloaded from Copernicus Marine Environment Monitoring Service (http://marine.copernicus.eu/; SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001 and SST_GLO_SST_L4_REP_OBSERVATIONS_010_011). Two model runs have been compared to the satellite values: the climate run as delivered to the Copernicus Climate Change Service - Monitoring and Forecasting Centre C3S-MFC, which was driven by a downscaled general circulation model (GCM), and a separate run driven by reanalysis data (referred to below as the validation run). Although the GCM-driven run cannot be expected to closely track the observations at time scales of days or months, the two should be comparable at longer time scales (e.g. the 15-year mean shown here).

Figure 1: (a) Seasonal mean surface chlorophyll-a for 2000 to 2015 for satellite ocean colour and model outputs; (b) model-satellite differences.

The model broadly reproduces the temporal and spatial patterns of chlorophyll concentration across the region – a northward-spreading spring bloom, highest levels in the northwestern European shelf in the April-June season (Figures 1, 2 and Table 1). However, model estimates tend to be higher than satellite, especially in regions of high chlorophyll. The two model runs give similar outputs, with the GCM-driven run tending to give greater overproduction compared to satellite. Model outputs are lower than satellite in the northern winter, however satellite data tends to be sparse for this period and region because of high cloud coverage, so has higher than usual uncertainty.

Furthermore, the satellite chlorophyll product used over-predicts in turbid shelf waters in winter, and we therefore should not assume that ERSEM under-predicts winter chlorophyll. Outputs from the GCM-driven model generally have a higher bias and root-mean-square-difference (RMSD) than the reanalysis- driven model. Additionally, the correlation to satellite data is weaker, which is expected since the climate model cannot reproduce the month-by-month conditions as closely as the reanalysis. The model-satellite correlation is weakest in shallow coastal areas (Figure 2), however satellite chlorophyll estimates are less reliable in these areas than for open seas. This apparent poor performance of the model in shallow regions is potentially partly because of the failure of the chlorophyll algorithm used by the satellite product to fully distinguish between chlorophyll and suspended particulate matter.

Figure 2: Spearman correlation between monthly mean and satellite values of surface chlorophyll, 2000-2015. The model runs are (a) validation (reanalysis-driven) (b) GCM-driven. Areas where the correlation is not statistically significant are marked in grey.

Table 1: Model-satellite comparison for surface chlorophyll-a, using monthly data for 2000 to 2015, for the whole model domain and sub-regions. Bias = model mean – satellite mean (mg m-3); RMSD = root mean square difference between model and satellite (mg m-3); Spearman-r = Spearman rank correlation coefficient.

There is also a good spatial and temporal match between modelled and satellite-derived sea surface temperature (Figures 3, 4 and Table 2). The reanalysis-driven run of the model tends to overestimate sea surface temperature in shelf waters, especially in the spring and summer. The GCM-driven run has higher bias, and over-estimates winter temperatures in much of the region.
This is in line with the biases in the GCM model driving the model. Model-satellite correlation is high in all regions, though rather lower for the GCM-driven run: as noted above, a climate model is not expected to accurately reproduce conditions month-by-month, even in a hindcast.

Figure 3 (a) Seasonal mean sea surface temperature for 2000 to 2015 for satellite and model outputs; (b) model-satellite difference.

Figure 4 Spearman correlation between monthly mean and satellite values of sea surface temperature, 2000-2015. The model runs are (a) validation (reanalysis-driven) (b) GCM-driven.

Table 2 Model-satellite comparison for surface temperature, using monthly data for 2000 to 2015, for the whole model domain and sub-regions. Bias = model mean – satellite mean (°C); RMSD = root mean square difference between model and satellite (°C); Spearman-r = Spearman rank correlation coefficient.

4. References

Butenschön, M., Clark, J., Aldridge, J. N., Icarus Allen, J., Artioli, Y., et al. (2016). ERSEM 15.06: A generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels. Geoscientific Model Development, 9(4), 1293–1339.

Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., … Vitart, F. (2011). The ERA-Interim reanalysis: Configuration and performance of the data assimilation system.
Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. https://doi.org/10.1002/qj.828
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., & Wimmer, W. (2012). The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sensing of Environment, 116, 140–158.

Locarnini, R.A., Mishonov, A.V., Antonov, J.I., Boyer, T.P., Garcia, H.E., Baranova, O.K., Zweng, M.M., Paver, C.R., Reagan, J.R., Johnson, D.R. and Hamilton, M., (2013). World ocean atlas 2013
MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., … Madec, G. (2015). Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Quarterly Journal of the Royal Meteorological Society, 141(689), 1072–1084. https://doi.org/10.1002/qj.2396

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