Different types of drifts in two seasonal forecast systems and their dependence on ENSO

Hermanson, Leon; Ren, Hong‐Li; Vellinga, Michael; Dunstone, Nick; Hyder, Pat; Ineson, Sarah; Scaife, Adam A.; Smith, Doug; Thompson, Vikki; Tian, Bing; Williams, Keith D.

doi:10.1007/s00382-017-3962-9

Cited by 43 publications

(42 citation statements)

References 53 publications

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“…Because models are imperfect, initialized forecasts drift away from the observations toward the biased model state (Boer et al, 2016;Gangstø et al, 2013;Hermanson et al, 2017;Smith, Eade, et al, 2013). For each individual decadal prediction system, a lead-time dependent drift has therefore been diagnosed from the hindcasts and removed from each month of the initialized model data to produce anomalies relative to the period 1971 to 2000.…”

Section: Modelsmentioning

confidence: 99%

Predicted Chance That Global Warming Will Temporarily Exceed 1.5 °C

Smith

Scaife

Hawkins

et al. 2018

Geophysical Research Letters

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The Paris Agreement calls for efforts to limit anthropogenic global warming to less than 1.5 °C above preindustrial levels. However, natural internal variability may exacerbate anthropogenic warming to produce temporary excursions above 1.5 °C. Such excursions would not necessarily exceed the Paris Agreement, but would provide a warning that the threshold is being approached. Here we develop a new capability to predict the probability that global temperature will exceed 1.5 °C above preindustrial levels in the coming 5 years. For the period 2017 to 2021 we predict a 38% and 10% chance, respectively, of monthly or yearly temperatures exceeding 1.5 °C, with virtually no chance of the 5‐year mean being above the threshold. Our forecasts will be updated annually to provide policy makers with advanced warning of the evolving probability and duration of future warming events.

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Section: Modelsmentioning

confidence: 99%

Predicted Chance That Global Warming Will Temporarily Exceed 1.5 °C

Smith

Scaife

Hawkins

et al. 2018

Geophysical Research Letters

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“…Moreover, they did not propose any strategy for correction. Therefore, to overcome the existing necessity of providing a complete diagnosis of model drifts globally (Hermanson et al, 2018), this work analyzes their spatiotemporal distribution for seasonal forecasts of temperature and precipitation worldwide. This can help to identify specific model deficits and offers the possibility of targeted improvement of certain processes formulation, resolution, and parametrization (Ehret et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Model Drifts in Seasonal Forecasting: Sensitivity to Ensemble Size and Implications for Bias Correction

Manzanas

2020

J Adv Model Earth Syst

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Despite its systematic presence in state‐of‐the‐art seasonal forecasts, the model drift (leadtime‐dependent bias) has been seldom studied to date. To fill this gap, this work analyzes its spatiotemporal distribution, and its sensitivity to the ensemble size in temperature and precipitation forecasts. Our results indicate that model continues to drift well beyond the first month after initialization, leading to significant, highly space‐ and time‐varying drifts over vast regions of the world. Nevertheless, small ensembles (less than 10 members) are enough to robustly estimate the mean model drift and its year‐to‐year fluctuations in skillful regions. Differently, in regions of low model skill, larger ensembles are required to appropriately characterize this interannual variability, which is often larger than the drift itself. This points out a necessity to develop new strategies that allow for efficiently dealing with model drift, especially when bias correcting seasonal forecasts—most of the techniques used to this aim rely on the assumption of stationary model errors. We demonstrate here that the use of moving windows can help to remove not only the mean forecast bias but also the unwanted effects coming out from the drift, which can lead to important intraseasonal biases if it is not properly taken into account. The results from this work can help to identify the nature and causes of some of the systematic errors in current coupled models and can have large implications for a wide community of users who need long, continuous unbiased seasonal forecasts to run their impact models.

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“…Forecast errors in seasonal predictions of the regional climate state are dominated by coupled oceanatmosphere model biases that lead to a systematic shift in the model climate state from the true climate state (Meehl et al, 2001; Kirtman and Pirani, 2008;Hermanson et al, 2018). Other significant sources of error are internal thermo-dynamical errors due to the chaotic nature of the coupled ocean-atmosphere system and to errors in the initial conditions for the forecasts (Frederiksen et al, 2010a, b;O'Kane et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…For example, in the atmosphere, the onset of blocking from a largely zonal flow state, or the reverse transition, is often associated with limited predictability and large forecast errors (Frederiksen et al, 2004 and references therein). Similarly, in the coupled system the transition in or out of an El Niño or La Niña state may have limited predictability while forecast skill may increase in these anomalous states before another regime transition occurs (Frederiksen et al, 2010a, b;Hermanson et al, 2018;O'Kane et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…This climate shift between the model and physical systems (resulting in climate drift of the simulations) is generally a dominant source of errors and forecast failures. Despite this our understanding of model biases and how to ameliorate them is rather limited (Hermanson et al, 2018) and the World Climate Research Group has made this a project of current and future focus.…”

Section: Introductionmentioning

confidence: 99%

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The role of climate drift in seasonal forecasts

2019

El Sawah, S. (Ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation.

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We devise a new method for determining the effects of model forcing anomalies and climate shifts on seasonal predictions. The role of model bias, due to anomalous forcings, and climate shift on forecast skill is studied for coupled ocean-atmosphere general circulation models (CGCMs) during different phases of the El Niño Southern Oscillation (ENSO) cycle. Biases due to anomalies or errors in the model forcing functions in simulations produce changes in the attractor of the thermo-dynamical system and shifts in the consequent climate state. We examine how such biases and climate shifts affect seasonal predictions. We employ an efficient intermediate complexity model with established forecast skill in a standard configuration and analyse seasonal predictability during a 20-year period of intense El Niño and La Niña events starting in January 1980 and ending December 2000. Firstly, we use reanalysed data from the observations to perform seasonal forecasts using our intermediate model with the optimised forcings for the standard configuration. Forecasts for one year are produced starting each month of the analysis period. The same initial conditions are then used by the intermediate complexity model, together with changed forcings that closely reproduce climates from selected complex CGCMs, to produce two additional control forecasts. We then determine the effects of the anomalous CGCM forcings, or model biases, and climate shifts, by comparing how well the forecasts with these configurations perform in situations of developing and large amplitude El Niños and La Niñas. We calculate the forecast error of the 50m ocean temperatures in the Pacific Ocean region and show the model's variability in forecast error growth during the annual cycle. Larger amplitudes of error are seen during the development of El Niño events in all cases, with the seasonal predictions with CGCM determined forcings showing evidence of drift towards the shifted model climate with overall increased forecast error.

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Different types of drifts in two seasonal forecast systems and their dependence on ENSO

Abstract: physical causes. Our results highlight the need to consider biases across a range of timescales in order to understand their causes and develop improved climate models.

Cited by 43 publications

References 53 publications

Predicted Chance That Global Warming Will Temporarily Exceed 1.5 °C

Predicted Chance That Global Warming Will Temporarily Exceed 1.5 °C

Assessment of Model Drifts in Seasonal Forecasting: Sensitivity to Ensemble Size and Implications for Bias Correction

The role of climate drift in seasonal forecasts

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