Climate-driven changes in the predictability of seasonal precipitation.

Climate-driven changes in precipitation amounts and their seasonal variability are expected in many continental-scale regions during the remainder of the 21st century. However, much less is known about future changes in the predictability of seasonal precipitation, an important earth system property relevant for climate adaptation. Here, on the basis of CMIP6 models that capture the present-day teleconnections between seasonal precipitation and previous-season sea surface temperature (SST), we show that climate change is expected to alter the SST-precipitation relationships and thus our ability to predict seasonal precipitation by 2100.

Hotspots of Predictability: Identifying Regions of High Precipitation Predictability at Seasonal Timescales From Limited Time Series Observations.

Precipitation prediction at seasonal timescales is important for planning and management of water resources as well as preparedness for hazards such as floods, droughts and wildfires. Quantifying predictability is quite challenging as a consequence of a large number of potential drivers, varying antecedent conditions, and small sample size of high-quality observations available at seasonal timescales, that in turn, increases prediction uncertainty and the risk of model overfitting. Here, we introduce a generalized probabilistic framework to account for these issues and assess predictability under uncertainty.

Underestimated MJO variability in CMIP6 models.

The Madden-Julian Oscillation (MJO) is the leading mode of intra-seasonal climate variability, having profound impacts on a wide range of weather and climate phenomena. Here, we use a wavelet-based spectral Principal Component Analysis (wsPCA) to evaluate the skill of 20 state-of-the-art CMIP6 models in capturing the magnitude and dynamics of the MJO. By construction, wsPCA has the ability to focus on desired frequencies and capture each propagative physical mode with one principal component (PC).

Zonally contrasting shifts of the tropical rainbelt in response to climate change.

Future changes in the position of the intertropical convergence zone (ITCZ; a narrow band of heavy precipitation in the tropics) with climate change could affect the livelihood and food security of billions of people. Although models predict a future narrowing of the ITCZ, uncertainties remain large regarding its future position, with most past work focusing on zonal-mean shifts. Here we use projections from 27 state-of-the-art (CMIP6) climate models and document a robust zonally-varying ITCZ response to the SSP3-7.

Rotated Spectral Principal Component Analysis (rsPCA) for Identifying Dynamical Modes of Variability in Climate Systems.

Spectral PCA (sPCA), in contrast to classical PCA, offers the advantage of identifying organized spatiotemporal patterns within specific frequency bands and extracting dynamical modes. However, the unavoidable trade-off between frequency resolution and robustness of the PCs leads to high sensitivity to noise and overfitting, which limits the interpretation of the sPCA results. We propose herein a simple nonparametric implementation of sPCA using the continuous analytic Morlet wavelet as a robust estimator of the cross-spectral matrices with good frequency resolution.

Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation.

Understanding the physical drivers of seasonal hydroclimatic variability and improving predictive skill remains a challenge with important socioeconomic and environmental implications for many regions around the world. Physics-based deterministic models show limited ability to predict precipitation as the lead time increases, due to imperfect representation of physical processes and incomplete knowledge of initial conditions. Similarly, statistical methods drawing upon established climate teleconnections have low prediction skill due to the complex nature of the climate system.

A new interhemispheric teleconnection increases predictability of winter precipitation in southwestern US.

Reliable prediction of seasonal precipitation in the southwestern US (SWUS) remains a challenge with significant implications for the economy, water security and ecosystem management of the region. Winter precipitation in the SWUS has been linked to several climate modes, including the El Niño-Southern Oscillation (ENSO), with limited predictive ability. Here we report evidence that late-summer sea surface temperature and geopotential height anomalies close to New Zealand exhibit higher correlation with SWUS winter precipitation than ENSO, enhancing the potential for earlier and more accurate prediction.