Atmospheric processes

Abstract:

It is often difficult to attribute uncertainty and errors in atmospheric models to designated model components. This is because sub-grid parameterised processes interact strongly with the large-scale transport represented by the explicit model dynamics. We carry out experiments with prescribed large-scale dynamics and different sub-grid physics suites. This dataset has been constructed for the Model Uncertainty Model Intercomparison Project (MUMIP), in which each suite forecasts sub-grid tendencies at a 22km grid. The common dynamics is derived from a convection-permitting benchmark: an ICON DYAMOND experiment (2.5km grid). We compare four different physics suites for atmospheric models in an Indian Ocean experiment. We analyse their joint PDFs of precipitation and associated physics tendencies for a full month, where precipitation is used to diagnose uncertainty of convective activity. We find that all physics suites produce very similar precipitation amounts, with very high correlations between models, i.e.,  > 0.95 at the native grid. However, the convection-permitting benchmark is more dissimilar from each of the physics suites, with correlations of  ≈ 0.80. Similarly, we show that the vertically averaged physics tendencies in the free-troposphere are highly similar between the four physics suites, yet different if reconstructed for the benchmark. The water vapour sink is very closely linked with precipitation in the four physics suites. This suggests that the coarse-grid models are overconfident. We hypothese is that variation in unresolved convective structures can lead to variation in the dynamics, following a given amount of latent heating at fine grids, but not in our physics suites. The difference appears to be caused by the explicit interactions between gravity waves, occurring at fine grids only Groot et al. (2024) We assess whether their non-linear feedback from convective precipitation systems explains our joint PDFs of precipitation. The slightly exponential curve supports the interaction mechanism. These findings are further evidence for a non-linear feedback between convective organisation/aggregation and dynamics. This feedback has been studied earlier in a real-case study with ICON by looking from the fixed-physics rather than the fixed-dynamics perspective. Our current results may indicate that sub-grid physics with stochastic physics perturbations emulate convective organisation effects.

Abstract:

<jats:title>Abstract</jats:title> <jats:p>Meaningful scores for forecast verification are essential for developing reliable forecasts, and there has been much effort to develop scores that align well with human perceptions of forecast quality. Whilst many of these scores have intuitive interpretations, relatively little is known about how these scores rank different forecasts, and how scores reflect forecast error. We theoretically explore the behaviour of two scores that fall within the ‘neighbourhood’ paradigm of spatial verification; the Fractions Skill Score (FSS) and Brier Divergence Skill Score (BDnSS). We investigate how each score ranks forecasts with two types of error; errors in the mean frequency (corresponding to intensity or shape errors) and errors in the standard deviation (corresponding to errors in spatial structure, such as blurring or excess noise). We find that under many situations the FSS assigns higher scores to forecasts that over-predict mean frequency, thus theoretically confirming the need to use the FSS with percentile thresholds. Both scores generally assign higher scores to forecasts with lower neighbourhood standard deviation, a reflection of the ‘double penalty’ problem; however, we observe that size of this effect is larger for the BDnSS than the FSS, showing that the FSS under some situations is less susceptible to the double penalty problem than the BDnSS.</jats:p>

Abstract:

Sea ice ensemble forecasts can be highly underdispersive, meaning that the ensemble spread is notably lower than the average forecast error. One common strategy to address underdispersion is to add stochastic perturbations to the forecasts. We detail the implementation of a stochastically perturbed parameterisation (SPP) scheme for SI³, the sea ice component used by the Integrated Forecast System (IFS), the forecast model used and developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We then evaluate its impact on seasonal forecasts of Northern Hemisphere summer and winter. The inclusion of SPP is found to enhance ensemble spread for sea ice concentration (SIC) and sea ice thickness (SIT) forecasts by around 10 % relative to a forecast with no SPP, which results in a better calibrated probabilistic forecast. Some small but robust changes to the mean state are also found, including a general decrease in the mean SIC and a redistribution of the winter ice from the central Arctic to the ice edge. These changes reduce or increase the mean bias depending on the region. Changes to the mean and spread of the sea ice result in changes to the mean and spread of air temperature up to at least 850 hPa, altering the mean air temperature biases of the model. An apparent consequence of this is a significant increase in the anomaly correlation coefficients of 500 hPa geopotential height (Z500) over the Euro-Atlantic domain in winter, which partially projects onto the North Atlantic Oscillation. We conclude that sea ice stochastic perturbations can be a valuable contribution to increased reliability of seasonal forecasts of the sea ice itself and can impact seasonal forecasts of the atmosphere at high and mid latitudes.