Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella
Abstract
:1. Introduction
2. Experimental Section
2.1. Study Details
2.2. Details about the Datasets
2.2.1. Downscaled Data
2.2.2. Baseline Observed Data
2.2.3. Global Climate Model Data
2.3. Geographical Areas of Investigation
2.4. Calculation Details and Summary Statistics
2.4.1. Baseline Characteristics of the Monthly Temperature Based Heat Indices for the Areas of Interest
2.4.2. Frequency of Hot Days (HD30)
2.5. Description of the Statistical Downscaling Methods
3. Results and Discussion
3.1. Observed Thermal Characteristics Based on Maurer02v2 Data
3.2. Comparison of the Period Mean of the Indices Calculated from Observed Data and the ARRM and BCCA Downscaled Ensembles
3.3. Analyses of the Representation of the Frequency of High Temperatures by Observed and Downscaled Data and Relation to Salmonella Occurrences
3.4. Association of Salmonella Occurrences with High Temperatures
4. Conclusions
- The ARRM and BCCA downscaling methods represented the period means of the number of hot days and hot nights for 1971–2000 well overall. This is expected since these downscaling methods are developed to adjust and bias-correct the means and the quantiles of the GCM data distributions to match the observed quantiles of daily tasmax or tasmin, in our case.
- The greatest differences between ARRM and BCCA are found in the peak of the summer season, July and August, when ARRM overestimated and BCCA often underestimated the observed 30-year period means of some indices, however, these differences are not evaluated for statistical significance.
- There is minimal difference in the results when counties or climate divisions, standard aggregation units of several counties, are used in our analyses.
- In April and May some of the CMIP3 bias-corrected and downscaled using both downscaling methods GCMs miss the increase in HD30 towards the summer months.
- The application of the BCCA complete downscaling methodology introduces additional uncertainty for some of the models during April and May (CNRM, MIROC-med, GFDL21).
- In light of this, the use of downscaled data from the significantly different GCMs to project future changes in the occurrence of Salmonella infections in April and May may lead to erroneous conclusions.
- The bias-correction corrects the large biases of the re-gridded GCM data and renders the temperature simulations viable for the evaluation of potential for Salmonella occurrences.
- The direct application of 2-degree re-gridded climate-model based indices is not advisable. The bias correction and downscaling obscure the knowledge on uncertainty in the GCM simulations.
- The characteristics of the variability of the statistically downscaled data and indices are inherited from the daily weather variability of the GCMs. The downscaling methods discussed here are not developed to correct for this variability. The sequences of weather patterns in global climate models are closely related to their abilities to represent various physical processes, such as blocking, that may bring about extended periods with high temperatures. Therefore, we draw attention to climate-model processes that need to be well represented to improve the salience of their application by practitioners.
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Index | Description |
---|---|
HD30 | Number of “hot” days with daily maximum temperature (tasmax) >30 °C |
HD35 | Number of “hot” days with tasmax >35 °C |
TR (tropical nights) | Number of “tropical” nights with daily minimum temperature (tasmin) >20 °C |
Data | Abbreviation | Resolution |
---|---|---|
ARRM downscaled GCMs tasmax, tasmin 20C3M experiment | ARRM_ensemble_1/8 | 1/8° lat × 1/8° lon (approx.12 km) |
BCCA downscaled GCMs tasmax, tasmin 20C3M experiment | BCCA_ensemble_1/8 | same |
Observed—Maurer02v2 tasmax, tasmin | Maurer02v2_1/8 | same |
Re-gridded GCM tasmax 20C3M experiment | GCM_2deg | 2° lat × 2° lon |
Bias-Corrected Re-gridded GCM tasmax 20C3M experiment | BC GCM_2deg | same |
Re-gridded Observed—Maurer02v1 tasmax | Maurer02v1_2deg | same |
Dataset | Maurer02v2_1/8 | Maurer02v1_2deg |
---|---|---|
ARRM_ensemble_1/8 | X | |
BCCA_ensemble_1/8 | X | |
GCM_2deg | X | |
BC GCM_2deg | X |
CMIP3 Model i.d. | Country | Atmosphere Model Component—Horizontal Resolution lat × lon |
---|---|---|
CGCM3.1(T47) | Canada | 3.75° × 3.75° |
CNRM-CM3 | France | 2.8° × 2.8° |
ECHAM5/MPI-OM | Germany | 1.9° × 1.9° |
ECHO-G | Germany/Korea | 3.75° × 3.75° |
GFDL-CM2.0 | USA | 2.0° × 2.5° |
GFDL-CM2.1 | USA | 2.0° × 2.5° |
MIROC3.2(medres) | Japan | approx. 2.8° × 2.8° |
MRI-CGCM2.3.2 | Japan | approx. 2.8° × 2.8° |
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Guentchev, G.S.; Rood, R.B.; Ammann, C.M.; Barsugli, J.J.; Ebi, K.; Berrocal, V.; O’Neill, M.S.; Gronlund, C.J.; Vigh, J.L.; Koziol, B.; et al. Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella. Int. J. Environ. Res. Public Health 2016, 13, 267. https://doi.org/10.3390/ijerph13030267
Guentchev GS, Rood RB, Ammann CM, Barsugli JJ, Ebi K, Berrocal V, O’Neill MS, Gronlund CJ, Vigh JL, Koziol B, et al. Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella. International Journal of Environmental Research and Public Health. 2016; 13(3):267. https://doi.org/10.3390/ijerph13030267
Chicago/Turabian StyleGuentchev, Galina S., Richard B. Rood, Caspar M. Ammann, Joseph J. Barsugli, Kristie Ebi, Veronica Berrocal, Marie S. O’Neill, Carina J. Gronlund, Jonathan L. Vigh, Ben Koziol, and et al. 2016. "Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella" International Journal of Environmental Research and Public Health 13, no. 3: 267. https://doi.org/10.3390/ijerph13030267