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This study examines the annual cycle of monsoon precipitation simulated by models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), then uses moist energy diagnostics to explain globally inhomogeneous projected future changes. Rainy season characteristics are quantified using a consistent method across the globe. Model bias is shown to include rainy season onsets tens of days later than observed in some monsoon regions (India, Australia, and North America) and overly large summer precipitation in others (North America, South America, and southern Africa). Projected next-century changes include rainy season lengthening in the two largest Northern Hemisphere monsoon regions (South Asia and central Sahel) and shortening in the two largest SouthernHemisphere regions (South America and southern Africa). Changes in the North American and Australian monsoons are less coherent across models. To understand these changes, relative moist static energy (MSE) is defined as the difference between local and tropical-mean surface airMSE. Future changes in relativeMSEin each region correlate well with onset and demise date changes. Furthermore, Southern Hemisphere regions projected to undergo rainy season shortening are spanned by an increasing equator-to-poleMSE gradient, suggesting their rainfallwill be increasingly inhibited by fluxes of dry extratropical air; Northern Hemisphere regionswith projected lengthening of rainy seasons undergo little change in equator-topole MSE gradient. Thus, although model biases raise questions as to the reliability of some projections, these results suggest that globally inhomogeneous future changes in monsoon timing may be understood through simple measures of surface air MSE.

This article presents a review of the scientific literature on detection, sources of variability, and predictability of the timing of monsoons. The timing of monsoons is characterized by the beginning (commonly referred to as onset) and end (commonly referred to as demise, cessation, retreat, or withdrawal) dates of the summer monsoons. The main methods used to detect the timing of monsoons are divided into two categories: local‐scale methods and regional to large‐scale methods. The sources of variability of the timing of monsoons are also separated into two categories: local‐scale and large‐scale sources. Finally, the article presents a summary of the literature on the predictability of the timing of monsoons using both dynamical and statistical approaches. We show that all methods are parameterized in some way. A comparison between two different methods shows that while there might be large differences in the definition of onset and demise dates at the local level, spatial aggregation usually reduces the noise and enhances the regional monsoonal signal, which may be predictable. [ABSTRACT FROM AUTHOR]

Retrospective decadal forecasts were undertaken using the Climate Forecast System version 2 (CFSv2) as part of Coupled Model Intercomparison Project 5. Decadal forecasts were performed separately by the National Center for Environmental Prediction (NCEP) and by the Center for Ocean-Land-Atmosphere Studies (COLA), with the centers using two different analyses for the ocean initial conditions the NCEP Climate Forecast System Reanalysis (CFSR) and the NEMOVAR-COMBINE analysis. COLA also examined the sensitivity to the inclusion of forcing by specified volcanic aerosols. Biases in the CFSv2 for both sets of initial conditions include cold midlatitude sea surface temperatures, and rapid melting of sea ice associated with warm polar oceans. Forecasts from the NEMOVAR-COMBINE analysis showed strong weakening of the Atlantic Meridional Overturning Circulation (AMOC), eventually approaching the weaker AMOC associated with CFSR. The decadal forecasts showed high predictive skill over the Indian, the western Pacific, and the Atlantic Oceans and low skill over the central and eastern Pacific. The volcanic forcing shows only small regional differences in predictability of surface temperature at 2m (T2m) in comparison to forecasts without volcanic forcing, especially over the Indian Ocean. An ocean heat content (OHC) budget analysis showed that the OHC has substantial memory, indicating potential for the decadal predictability of T2m; however, the model has a systematic drift in global mean OHC. The results suggest that the reduction of model biases may be the most productive path towards improving the model's decadal forecasts. [ABSTRACT FROM AUTHOR]