Dataset Information

Keeping it simple: the value of an irreducibly simple climate model.

ABSTRACT: Richardson et al. (Sci Bull, 2015. doi:10.1007/s11434-015-0806-z) suggest that the irreducibly simple climate model described in Monckton of Brenchley et al. (Sci Bull 60:122-135, 2015. doi:10.1007/s11434-014-0699-2) was not validated against observations, relying instead on synthetic test data based on underestimated global warming, illogical parameter choice and near-instantaneous response at odds with ocean warming and other observations. However, the simple model, informed by its authors' choice of parameters, usually hindcasts observed temperature change more closely than the general-circulation models, and finds high climate sensitivity implausible. With IPCC's choice of parameters, the model is further validated in that it duly replicates IPCC's sensitivity interval. Also, fast climate system response is consistent with near-zero or net-negative temperature feedback. Given the large uncertainties in the initial conditions and evolutionary processes determinative of climate sensitivity, subject to obvious caveats a simple sensitivity-focused model need not, and the present model does not, exhibit significantly less predictive skill than the general-circulation models.

SUBMITTER: Monckton Of Brenchley C

PROVIDER: S-EPMC4534509 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Keeping it simple: the value of an irreducibly simple climate model.

Monckton Of Brenchley Christopher C Soon Willie W-H WW Legates David R DR Briggs William M WM

Science bulletin 20150806 15

Richardson et al. (Sci Bull, 2015. doi:10.1007/s11434-015-0806-z) suggest that the irreducibly simple climate model described in Monckton of Brenchley et al. (Sci Bull 60:122-135, 2015. doi:10.1007/s11434-014-0699-2) was not validated against observations, relying instead on synthetic test data based on underestimated global warming, illogical parameter choice and near-instantaneous response at odds with ocean warming and other observations. However, the simple model, informed by its authors' ch ...[more]

PMID: 26301116

Similar Datasets

Project description:Given the crucial role of climate in malaria transmission, many mechanistic models of malaria represent vector biology and the parasite lifecycle as functions of climate variables in order to accurately capture malaria transmission dynamics. Lower dimension mechanistic models that utilize implicit vector dynamics have relied on indirect climate modulation of transmission processes, which compromises investigation of the ecological role played by climate in malaria transmission. In this study, we develop an implicit process-based malaria model with direct climate-mediated modulation of transmission pressure borne through the Entomological Inoculation Rate (EIR). The EIR, a measure of the number of infectious bites per person per unit time, includes the effects of vector dynamics, resulting from mosquito development, survivorship, feeding activity and parasite development, all of which are moderated by climate. We combine this EIR-model framework, which is driven by rainfall and temperature, with Bayesian inference methods, and evaluate the model's ability to simulate local transmission across 42 regions in Rwanda over four years. Our findings indicate that the biologically-motivated, EIR-model framework is capable of accurately simulating seasonal malaria dynamics and capturing of some of the inter-annual variation in malaria incidence. However, the model unsurprisingly failed to reproduce large declines in malaria transmission during 2018 and 2019 due to elevated anti-malaria measures, which were not accounted for in the model structure. The climate-driven transmission model also captured regional variation in malaria incidence across Rwanda's diverse climate, while identifying key entomological and epidemiological parameters important to seasonal malaria dynamics. In general, this new model construct advances the capabilities of implicitly-forced lower dimension dynamical malaria models by leveraging climate drivers of malaria ecology and transmission.

Dataset Information

Keeping it simple: the value of an irreducibly simple climate model.

Publications

Keeping it simple: the value of an irreducibly simple climate model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets