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In late December of 2022 and the first half of January 2023, an unprecedented series of atmospheric rivers (ARs) produced near-record heavy rains and flooding over much of California. Here, we employ the NASA GEOS AGCM run in a "replay" mode, together with more idealized simulations with a stationary wave model, to identify the remote forcing regions, mechanisms, and underlying predictability of this flooding event. In particular, the study addresses the underlying causes of a persistent positive Pacific–North American (PNA)-like circulation pattern that facilitated the development of the ARs. We show that the pattern developed in late December as a result of vorticity forcing in the North Pacific jet exit region. We further provide evidence that this vorticity forcing was the result of a chain of events initiated in mid-December with the development of a Rossby wave (as a result of forcing linked to the MJO) that propagated from the northern Indian Ocean into the North Pacific. As such, both the initiation of the event and the eventual development of the PNA depended critically on internally generated Rossby wave forcings, with the North Pacific jet playing a key role. This, combined with contemporaneous SST (La Niña) forcing that produced a circulation response in the AGCM that was essentially opposite to the positive PNA, underscores the fundamental lack of predictability of the event at seasonal time scales. Forecasts produced with the GEOS-coupled model suggest that useful skill in predicting the PNA and extreme precipitation over California was in fact limited to lead times shorter than about 3 weeks. [ABSTRACT FROM AUTHOR]

The second West African Monsoon Modeling and Evaluation Project Experiment (WAMME II) is designed to improve understanding of the possible roles and feedbacks of sea surface temperature (SST), land use land cover change (LULCC), and aerosols forcings in the Sahel climate system at seasonal to decadal scales. The project's strategy is to apply prescribed observationally based anomaly forcing, i.e., "idealized but realistic" forcing, in simulations by climate models. The goal is to assess these forcings' effects in producing/amplifying seasonal and decadal climate variability in the Sahel between the 1950s and the 1980s, which is selected to characterize the great drought period of the last century. This is the first multi-model experiment specifically designed to simultaneously evaluate such relative contributions. The WAMME II models have consistently demonstrated that SST forcing is a major contributor to the 20th century Sahel drought. Under the influence of the maximum possible SST forcing, the ensemble mean of WAMME II models can produce up to 60% of the precipitation difference during the period. The present paper also addresses the role of SSTs in triggering and maintaining the Sahel drought. In this regard, the consensus of WAMME II models is that both Indian and Pacific Ocean SSTs greatly contributed to the drought, with the former producing an anomalous displacement of the Intertropical Convergence Zone (ITCZ) before the WAM onset, and the latter mainly contributes to the summer WAM drought. The WAMME II models also show that the impact of LULCC forcing on the Sahel climate system is weaker than that of SST forcing, but still of first order magnitude. According to the results, under LULCC forcing the ensemble mean of WAMME II models can produces about 40% of the precipitation difference between the 1980s and the 1950s. The role of land surface processes in responding to and amplifying the drought is also identified. The results suggest that catastrophic consequences are likely to occur in the regional Sahel climate when SST anomalies in individual ocean basins and in land conditions combine synergistically to favor drought.

The relationship between leading atmospheric teleconnection patterns and Greenland Ice Sheet (GrIS) temperature, precipitation, and surface mass balance (SMB) are investigated for the last 36 summers (1979-2014) based on Modern-Era Retrospective analysis for Research and Applications version 2 reanalyses. The results indicate that the negative phase of both the North Atlantic Oscillation (NAO) and Arctic Oscillation, associated with warm and dry conditions for the GrIS, lead to SMB decreases within 0-1 months. Furthermore, the positive phase of the East Atlantic (EA) pattern often lags the negative NAO, reflecting a dynamical linkage between these modes that acts to further enhance the warm and dry conditions over the GrIS, leading to a favorable environment for enhanced surface mass loss. The development of a strong negative NAO in combination with a strong positive EA in recent years leads to significantly larger GrIS warming compared to when the negative NAO occurs in combination with a negative or weak positive EA (0.69 K versus 0.13 K anomaly). During 2009 and 2011, weakened (as compared to conditions during the severe surface melt cases of 2010 and 2012) local high pressure blocking produced colder northerly flow over the GrIS inhibiting warming despite the occurrence of a strong negative NAO, reflecting an important role for the EA during those years. In particular, the EA acts with the NAO to enhance warming in 2010 and 2012, and weaken high pressure blocking in 2009 and 2011. In general, high pressure blocking primarily impacts the western areas of the GrIS via advective temperature increases, while changes in net surface radiative fluxes account for both western and eastern GrIS temperature changes.

Although a theory of the climatology of tropical cyclone formation remains elusive, high-resolution climate models can now simulate many aspects of tropical cyclone climate.

The 20/7 northern High Plains precipitation deficits were largely the result of internal atmospheric variability. Global warming may have exacerbated the dry condition by producing surface warming and increasing the [...]

Some of the long-term changes in weather and climate extremes across the US have occurred as expected in the warming climate, but the trends vary throughout the country and easily detected due to multiyear and decadal variations. The US National Climate Assessment has to address extremes, as these are changing and anthropogenic climate change has a role in altering the probabilities of some of the extreme events. Extreme events also drive changes in natural and human systems more than average climatic conditions. Four workshops have been held to provide technical input to the US National Climate Assessment writing team where leading scientists in the field worked jointly to determine how best to assess the state of the science in understanding the decadal- to century-scale variability and changes in various types of extreme events.

In order to improve seasonal-to-interannual precipitation forecasts and their application by decision makers, there is a clear need to understand when, where, and to what extent seasonal precipitation anomalies are driven by potentially predictable surface–atmosphere interactions versus to chaotic interannual atmospheric dynamics. Using a simple Monte Carlo approach, interannual variability and linear trends in the SST-forced signal and potential predictability of boreal winter precipitation anomalies is examined in an ensemble of twentieth century AGCM simulations. Signal and potential predictability are shown to be non-stationary over more than 80% of the globe, while chaotic noise is shown to be stationary over most of the globe. Correlation analysis with respect to magnitudes of the four leading modes of global SST variability suggests that interannual variability and trends in signal and potential predictability over 35% of the globe is associated with ENSO-related SST variability; signal and potential predictability are not significantly associated with SST modes characterized by a global SST trend, North Atlantic SST variability, and North Pacific SST variability, respectively. Results suggest that mechanisms other than SST variability contribute to the non-stationarity of signal and noise characteristics of hydroclimatic variability over mid- and high-latitude regions.

This paper briefly presents the West African Monsoon (WAM) Modeling and Evaluation Project (WAMME) and evaluates WAMME general circulation models’ (GCM) performances in simulating variability of WAM precipitation, surface temperature, and major circulation features at seasonal and intraseasonal scales in the first WAMME experiment. The analyses indicate that models with specified sea surface temperature generally have reasonable simulations of the pattern of spatial distribution of WAM seasonal mean precipitation and surface temperature as well as the averaged zonal wind in latitude-height cross-section and low level circulation. But there are large differences among models in simulating spatial correlation, intensity, and variance of precipitation compared with observations. Furthermore, the majority of models fail to produce proper intensities of the African Easterly Jet (AEJ) and the tropical easterly jet. AMMA Land Surface Model Intercomparison Project (ALMIP) data are used to analyze the association between simulated surface processes and the WAM and to investigate the WAM mechanism. It has been identified that the spatial distributions of surface sensible heat flux, surface temperature, and moisture convergence are closely associated with the simulated spatial distribution of precipitation; while surface latent heat flux is closely associated with the AEJ and contributes to divergence in AEJ simulation. Common empirical orthogonal functions (CEOF) analysis is applied to characterize the WAM precipitation evolution and has identified a major WAM precipitation mode and two temperature modes (Sahara mode and Sahel mode). Results indicate that the WAMME models produce reasonable temporal evolutions of major CEOF modes but have deficiencies/uncertainties in producing variances explained by major modes. Furthermore, the CEOF analysis shows that WAM precipitation evolution is closely related to the enhanced Sahara mode and the weakened Sahel mode, supporting the evidence revealed in the analysis using ALMIP data. An analysis of variability of CEOF modes suggests that the Sahara mode leads the WAM evolution, and divergence in simulating this mode contributes to discrepancies in the precipitation simulation.