Predictability of weather and climate

Abstract:

Heatwaves constitute a major threat to human health and ecosystems. Projected increases in heatwave frequency and severity thus lead to the need for prediction to enhance preparedness and minimize adverse impacts. In this Review, we document current capabilities for heatwave prediction at daily to decadal timescales and outline projected changes under anthropogenic warming. Various local and remote drivers and feedbacks influence heatwave development. On daily timescales, extratropical atmospheric blocking and global land–atmosphere coupling are most pertinent, and on subseasonal to seasonal timescales, soil moisture and ocean surface anomalies contribute. Knowledge of these drivers allows heatwaves to be skilfully predicted at daily to weekly lead times. Predictions are challenging beyond timescales of a few weeks, but tendencies for above-average temperatures can be estimated. Further into the future, heatwaves are anticipated to become more frequent, persistent and intense in nearly all inhabited regions, with trends amplified by soil drying in some areas, especially the mid-latitudes. There is also an increased occurrence of humid heatwaves, especially in southern Asia. A better understanding of the relevant drivers and their model representation, including atmospheric dynamics, atmospheric and soil moisture, and surface cover should be prioritized to improve heatwave prediction and projection.

Abstract:

In order to explore temporal changes of predictability of ENSO, a novel set of global biennial climate reforecasts for the historical period 1901 – 2010 has been generated using a modern initialized coupled forecasting system. We find distinct periods of enhanced long-range skill at the beginning and end of the 20th century and an extended multi37 decadal epoch of reduced skill during the 1930s-1950s. Once the forecast skill extends beyond the first spring barrier, the predictability limit is much enhanced and our results provide support for the feasibility of skilful ENSO forecasts up to 18 months. Changes in the mean state, variability (amplitude), persistence, seasonal cycle and predictability suggest that multi-decadal variations in the dynamical characteristics of ENSO rather than the data coverage and quality of the observations have primarily driven the reported non43 monotonic skill modulations.

Abstract:

This study presents an approach to provide seamless climate information by concatenating decadal climate predictions and climate projections in time. Results for near-surface air temperature over 29 regions indicate that such an approach has potential to provide meaningful information but can also introduce significant inconsistencies. Inconsistencies are often most pronounced for relatively extreme quantiles of the CMIP6 multi-model ensemble distribution, whereas they are generally smaller and mostly insignificant for quantiles close to the median. The regions most affected are the North Atlantic, Greenland and Northern Europe. Two potential ways to reduce inconsistencies are discussed, including a simple calibration method and a weighting approach based on model performance. Calibration generally reduces inconsistencies but does not eliminate all of them. The impact of model weighting is minor, which is found to be linked to the small size of the decadal climate prediction ensemble, which in turn limits the applicability of that method.