Another Example Of The Misuse Of Multi-Decadal Global Climate Model Predictions

I have posted numerous times on the lack of scientific robustness in using multi-decadal global climate model predictions for impact studies (e.g. see and see). On Wednesday I posted a guest contribution from Geert Jan van Oldenborgh (see) which reported finding only very limited decadal predictive skill for a few geographic areas.  Their research provides further confirmation of our conclusions in

Pielke Sr., R.A., and R.L. Wilby, 2012: Regional climate downscaling – what’s the point? Eos Forum,  93, No. 5, 52-53, doi:10.1029/2012EO050008

that [where type 4 downscaling refers to multi-decadal climate predictions]

“…..for regional downscaling (and global) models to add value (beyond what is available to the impacts community via the historical, recent paleorecord and a worst case sequence of days), they must be able to skillfully predict changes in regional weather statistics in response to human climate forcings. This is a greater challenge than even skillfully simulating current weather statistics…….It is therefore inappropriate to present type 4 results to the impacts community as reflecting more than a subset of possible future climate risks.”

There is a seminar tomorrow that exemplifies the continued misuse of these model results.  The announcement for the talk reads [highlight added]

Fri Mar 09,          03:30 pm.                    NOTE LOCATION:  IBS Building, room 155

Catching Climate Fever: Diagnosing the Changing Environment of Infectious Disease?

Andrew Comrie

University of Arizona, Associate Vice President for Research and Dean of the Graduate College and School of Geography and Development×70.png

Andrew Comrie

University of Arizona, Associate Vice President for Research and Dean of the Graduate College and School of Geography and Development

Abstract: What are the disease impacts of climate change? Climate-health research is a rapidly expanding field that intersects multiple disciplines and approaches. I review key concepts using a range of infectious disease examples from my own research group, and then focus on mosquito vectors of disease in the US. Climate and climate change affect the ecology of infectious diseases via disease hosts, vectors, and reservoirs. In the southern United States, Culex quinquefasciatus may be a primary vector for West Nile Virus (WNV) in many locations because of its affinity for urban environments and because they feed on humans and bird hosts. Changes in temperature and precipitation regimes may affect the population and season length of this mosquito, altering the risk of WNV transmission to humans. It is difficult to assess the impact of climate changes on local mosquito populations, given the paucity of observations, and therefore we have developed the Dynamic Mosquito Simulation Model (DyMSiM). Using downscaled general circulation model (GCM) projections for current and future conditions, we modeled mosquito population dynamics across the southern United States using DyMSiM. As expected, modeled mosquito populations responded differently by location, season, and time period. Temperature changes have a marked positive effect on mosquito population in fall, while summer showed a strong positive link between precipitation and mosquito populations. Higher temperatures led to a rise in populations during the cooler months, but during summer decreased breeding habitats due to drying from evaporation. In the Western US, projected drier conditions did not decrease mosquito populations because they rely heavily on permanent water sources which are not controlled by precipitation.

Refreshments following lecture on the IBS patio.

This lecture series was made possible by the generous support from The Beirne Carter Foundation.

Co-sponsored by the Environment and Society Program of the Institute of Behavioral Science and CIRES Center for Science and Technology Policy Research.

While the sensitivity of mosquito populations to weather and climate part of the talk may be quite interesting and robust, the claim to be able to predict changes in these populations in coming decades due to changes in the statistics of the climate system is yet another inappropriate use of climate modeling.

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