Atmospheric processes

Abstract:

While weather forecasting models can be tested by performing and evaluating many hindcasts, the limited observational record restricts the degree to which climate projections can be evaluated. Therefore a question of interest is: to what degree can we evaluate the potential skill of a climate model's response to forcing by assessing the reliability of short‐range weather and seasonal forecasts produced by the same model? We address this question using a dynamical systems framework. We use linear response theory to provide the mean climate response of a general dynamical system to a small external forcing. We relate this response to the reliability of initial value forecasts. We find that, in order to capture the mean climate response, the forecast model must correctly represent the slowest evolving modes of variability in the system. The reliability of forecasts on seasonal and longer time‐scales, which is sensitive to the representation of these slow modes, could therefore indicate if the forecast model has the correct climate sensitivity and so will respond correctly to an applied external forcing. In this way, the skill of initialized forecasts could act as an ‘emergent constraint’ on climate sensitivity. However, we also highlight that unreliable seasonal forecasts do not necessarily indicate an incorrect climate projection. This is because correctly representing rapidly evolving modes is also necessary for reliable seasonal forecasts.

Abstract:

Subgrid-scale (SGS) velocity variations result in gridscale sea surface flux enhancements that must be parameterized in weather and climate models. Traditional parameterizations are deterministic in that they assign a unique value of the SGS velocity flux enhancement to any given configuration of the resolved state. In this study, we assess the statistics of SGS velocity flux enhancement over a range of averaging scales (as a proxy for varying model resolution) through systematic coarse-graining of a convection-permitting atmospheric model simulation over the Indian Ocean and west Pacific warm pool. Conditioning the statistics of the SGS velocity flux enhancement on 1) the fluxes associated with the resolved winds and 2) the precipitation rate, we find that the lack of a separation between “resolved” and “unresolved” scales results in a distribution of flux enhancements for each configuration of the resolved state. That is, the SGS velocity flux enhancement should be represented stochastically rather than deterministically. The spatial and temporal statistics of the SGS velocity flux enhancement are investigated by using basic descriptive statistics and through a fit to an anisotropic space–time covariance structure. Potential spatial inhomogeneities of the statistics of the SGS velocity flux enhancement are investigated through regional analysis, although because of the relatively short duration of the simulation (9 days) distinguishing true inhomogeneity from sampling variability is difficult. Perspectives for the implementation of such a stochastic parameterization in weather and climate models are discussed.

Abstract:

© 2018 Royal Meteorological Society Studies conducted by the UK Met Office reported significant skill in predicting the winter North Atlantic Oscillation (NAO) index with their seasonal prediction system. At the same time, a very low signal-to-noise ratio was observed, as measured using the “ratio of predictable components” (RPC) metric. We analyse both the skill and signal-to-noise ratio using a new statistical toy model, which assumes NAO predictability is driven by regime dynamics. It is shown that if the system is approximately bimodal in nature, with the model consistently underestimating the level of regime persistence each season, then both the high skill and high RPC value of the Met Office hindcasts can easily be reproduced. Underestimation of regime persistence could be attributable to any number of sources of model error, including imperfect regime structure or errors in the propagation of teleconnections. In particular, a high RPC value for a seasonal mean prediction may be expected even if the model's internal level of noise is realistic.

Abstract:

The impact of stochastic physics on El Niño Southern Oscillation (ENSO) is investigated in the EC-Earth coupled climate model. By comparing an ensemble of three members of control historical simulations with three ensemble members that include stochastics physics in the atmosphere, we find that in EC-Earth the implementation of stochastic physics improves the excessively weak representation of ENSO. Specifically, the amplitude of both El Niño and, to a lesser extent, La Niña increases. Stochastic physics also ameliorates the temporal variability of ENSO at interannual time scales, demonstrated by the emergence of peaks in the power spectrum with periods of 5–7 years and 3–4 years. Based on the analogy with the behaviour of an idealized delayed oscillator model (DO) with stochastic noise, we find that when the atmosphere–ocean coupling is small (large) the amplitude of ENSO increases (decreases) following an amplification of the noise amplitude. The underestimated ENSO variability in the EC-Earth control runs and the associated amplification due to stochastic physics could be therefore consistent with an excessively weak atmosphere–ocean coupling. The activation of stochastic physics in the atmosphere increases westerly wind burst (WWB) occurrences (i.e. amplification of noise amplitude) that could trigger more and stronger El Niño events (i.e. increase of ENSO oscillation) in the coupled EC-Earth model. Further analysis of the mean state bias of EC-Earth suggests that a cold sea surface temperature (SST) and dry precipitation bias in the central tropical Pacific together with a warm SST and wet precipitation bias in the western tropical Pacific are responsible for the coupled feedback bias (weak coupling) in the tropical Pacific that is related to the weak ENSO simulation. The same analysis of the ENSO behaviour is carried out in a future scenario experiment (RCP8.5 forcing), highlighting that in a coupled model with an extreme warm SST, characterized by a strong coupling, the effect of stochastic physics on the ENSO representation is opposite. This corroborates the hypothesis that the mean state bias of the tropical Pacific region is the main reason for the ENSO representation deficiency in EC-Earth.