Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall

Nachiketa Acharya Carlo Montes Timothy Joseph Krupnik (2023, [Artículo])

Bangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate-risk-prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi-model ensemble (CMME) of seven state-of-the-art general circulation models (GCMs) from the North American Multi-Model Ensemble project. The lead-1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA-based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real-time seasonal rainfall forecasts using this new forecast system.

Multi-Model Ensemble Seasonal Forecasting CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE SERVICES FORECASTING MONSOONS

Using Observed Residual Error Structure Yields the Best Estimates of Individual Growth Parameters

Marcelo Vidal Curiel Bernal EUGENIO ALBERTO ARAGON NORIEGA MIGUEL ANGEL CISNEROS MATA LAURA SANCHEZ VELASCO SYLVIA PATRICIA ADELHEID JIMENEZ ROSENBERG ALEJANDRO FRANCISCO PARES SIERRA (2021, [Artículo])

"Obtaining the best possible estimates of individual growth parameters is essential in studies of physiology, fisheries management, and conservation of natural resources since growth is a key component of population dynamics. In the present work, we use data of an endangered fish species to demonstrate the importance of selecting the right data error structure when fitting growth models in multimodel inference. The totoaba (Totoaba macdonaldi) is a fish species endemic to the Gulf of California increasingly studied in recent times due to a perceived threat of extinction. Previous works estimated individual growth using the von Bertalanffy model assuming a constant variance of length-at-age. Here, we reanalyze the same data under five different variance assumptions to fit the von Bertalanffy and Gompertz models. We found consistent significant differences between the constant and nonconstant error structure scenarios and provide an example of the consequences using the growth performance index _0 to show how using the wrong error structure can produce growth parameter values that can lead to biased conclusions. Based on these results, for totoaba and other related species, we recommend using the observed error structure to obtain the individual growth parameters."

multimodel inference, error structure, totoaba, growth performance BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA ANIMAL (ZOOLOGÍA) FISIOLOGÍA ANIMAL FISIOLOGÍA ANIMAL