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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. Specifically, in the tropics, seasonal precipitation predictability from SSTs is projected to increase throughout the year, except the northern Amazonia during boreal winter. Concurrently, in the extra-tropics predictability is likely to increase in central Asia during boreal spring and winter. The altered predictability, together with enhanced interannual variability of seasonal precipitation, poses new opportunities and challenges for regional water management.

 

Abstract 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. To improve the interpretability of the results, especially when several modes of similar amplitude exist within the same frequency band, we propose a rotation of the complex-valued eigenvectors to optimize their spatial regularity (smoothness). The developed method, called rotated spectral PCA (rsPCA), is tested on synthetic data simulating propagating waves and shows impressive performance even with high levels of noise in the data. Applied to global historical geopotential height (GPH) and sea surface temperature (SST) daily time series, the method accurately captures patterns of atmospheric Rossby waves at high frequencies (3–60-day periods) in both GPH and SST and El Niño–Southern Oscillation (ENSO) at low frequencies (2–7-yr periodicity) in SST. At high frequencies the rsPCA successfully unmixes the identified waves, revealing spatially coherent patterns with robust propagation dynamics. 

The Madden‐Julian Oscillation (MJO) is the leading mode of intraseasonal 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). We show that the MJO contribution to the total intraseasonal climate variability is substantially underestimated in most CMIP6 models. The joint distribution of the modulus and angular frequency of the wavelet PC series associated with MJO is used to rank models relatively to the observations through the Wasserstein distance. Using Hovmöller phase‐longitude diagrams, we also show that precipitation variability associated with MJO is underestimated in most CMIP6 models for the Amazonia, Southwest Africa, and Maritime Continent.

 

Soil organic carbon (SOC) is going through rapid reorganization due to anthropogenic influences. Understanding how biogeochemical transformation and erosion‐induced SOC redistribution influence SOC profiles and stocks is critical to our food security and adaptation to climate change. The important roles of erosion and deposition on SOC dynamics have drawn increasing attention in the past decades, but quantifying such dynamics is still challenging. Here we develop a process‐based quasi 3‐D model that couples surface runoff, soil moisture dynamics, biogeochemical transformation, and landscape evolution. We apply this model to a subcatchment in Iowa to understand how natural forcing and farming practices affect the SOC dynamics in the critical zone. The net soil thickness and SOC stock change rates are −0.336 (mm/yr) and −1.9 (g C/m²/year), respectively. Our model shows that in a fast transport landscape, SOC transport is the dominant control on SOC dynamics compared to biogeochemical transformation. The SOC profiles have “noses” below the surface at depositional sites, which are consistent with cores sampled at the same site. Generally, erosional sites are local net atmospheric carbon sinks and vice versa for depositional sites, but exceptions exist as seen in the simulation results. Furthermore, the mechanical soil mixing arising from tillage enhances SOC stock at erosional sites and reduces it at depositional ones. This study not only helps us understand the evolution of SOC stock and profiles in a watershed but can also serve as an instrument to develop practical means for protecting carbon loss due to human activities.