Climate model trend errors are evident in seasonal forecasts at short leads DOI Creative Commons
Jonathan D. Beverley, Matthew Newman, Andrew Hoell

et al.

npj Climate and Atmospheric Science, Journal Year: 2024, Volume and Issue: 7(1)

Published: Nov. 20, 2024

Climate models exhibit errors in their simulation of historical trends variables including sea surface temperature, winds, and precipitation, with important implications for regional global climate projections. Here, we show that the same trend are also present a suite initialised seasonal re-forecasts years 1993–2016. These produced by operational similar to Coupled Model Intercomparison Project (CMIP)-class share external forcings (e.g. CO2/aerosols). The errors, which often well-developed at very short lead times, represent roughly linear change model mean biases over 1993–2016 re-forecast record. similarity both simulations suggests likewise result from evolving biases, responding changing radiative forcings, instead being an erroneous long-term response forcing. Therefore, these may be investigated examining short-lead development forecasts/re-forecasts, suggest should made all CMIP models.

Language: Английский

Impact of the ocean in-situ observations on the ECMWF seasonal forecasting system DOI Creative Commons
Magdalena Balmaseda, Beena Balan Sarojini, Michael Mayer

et al.

Frontiers in Marine Science, Journal Year: 2024, Volume and Issue: 11

Published: Sept. 12, 2024

This study aims to evaluate the impact of in-situ ocean observations on seasonal forecasts. A series reforecasts have been conducted for period 1993-2015, in which different sets were withdrawn production initial conditions, while maintaining a strong constrain sea surface temperature (SST). By comparing reforecast sets, it is possible assess forecast and atmospheric variables. Results show that profound significant impacts mean state variables, can be classified into categories: i) due local air-sea interaction, as direct consequence changes mixed layer visible early stages forecasts; ii) dynamical balances, most Equatorial Pacific forecasts initialized May, amplify evolve with lead time; iii) circulation resulting from large scale SST gradients; these are non-local, mediated by bridge, they obvious removing Atlantic basin only global circulation; iv) tropical deep convection associated structure warm pools. The also representation trends affect observing system extratropics appears dominated Argo, but this not case Tropical Pacific, where other systems play role constraining state.

Language: Английский

Citations

1
Climate model trend errors are evident in seasonal forecasts at short leads DOI Creative Commons
Jonathan D. Beverley, Matthew Newman, Andrew Hoell

et al.

npj Climate and Atmospheric Science, Journal Year: 2024, Volume and Issue: 7(1)

Published: Nov. 20, 2024

Climate models exhibit errors in their simulation of historical trends variables including sea surface temperature, winds, and precipitation, with important implications for regional global climate projections. Here, we show that the same trend are also present a suite initialised seasonal re-forecasts years 1993–2016. These produced by operational similar to Coupled Model Intercomparison Project (CMIP)-class share external forcings (e.g. CO2/aerosols). The errors, which often well-developed at very short lead times, represent roughly linear change model mean biases over 1993–2016 re-forecast record. similarity both simulations suggests likewise result from evolving biases, responding changing radiative forcings, instead being an erroneous long-term response forcing. Therefore, these may be investigated examining short-lead development forecasts/re-forecasts, suggest should made all CMIP models.

Language: Английский

Citations

1