Marine Heatwaves in the Chesapeake Bay

Prolonged events of anomalously warm sea water temperature, or marine heatwaves (MHWs), have major detrimental effects to marine ecosystems and the world's economy. While frequency, duration and intensity of MHWs have been observed to increase in the global oceans, little is known about their potential occurrence and variability in estuarine systems due to limited data in these environments. In the present study we analyzed a novel data set with over three decades of continuous in situ temperature records to investigate MHWs in the largest and most productive estuary in the US: the Chesapeake Bay. MHWs occurred on average twice per year and lasted 11 days, resulting in 22 MHW days per year in the bay. Average intensities of MHWs were 3°C, with maximum peaks varying between 6 and 8°C, and yearly cumulative intensities of 72°C × days on average. Large co-occurrence of MHW events was observed between different regions of the bay (50–65%), and also between Chesapeake Bay and the Mid-Atlantic Bight (40–50%). These large co-occurrences, with relatively short lags (2–5 days), suggest that coherent large-scale air-sea heat flux is the dominant driver of MHWs in this region. MHWs were also linked to large-scale climate modes of variability: enhancement of MHW days in the Upper Bay were associated with the positive phase of Niño 1+2, while enhancement and suppression of MHW days in both the Mid and Lower Bay were associated with positive and negative phases of North Atlantic Oscillation, respectively. Finally, as a result of long-term warming of the Chesapeake Bay, significant trends were detected for MHW frequency, MHW days and yearly cumulative intensity. If these trends persist, by the end of the century the Chesapeake Bay will reach a semi-permanent MHW state, when extreme temperatures will be present over half of the year, and thus could have devastating impacts to the bay ecosystem, exacerbating eutrophication, increasing the severity of hypoxic events, killing benthic communities, causing shifts in species composition and decline in important commercial fishery species. Improving our basic understanding of MHWs in estuarine regions is necessary for their future predictability and to guide management decisions in these valuable environments.

Weakened Interannual Variability in the Tropical Atlantic

Climate variability in the Tropical Atlantic is complex with strong ocean-atmosphere coupling, where the sea surface temperature (SST) variability impacts the hydroclimate of the surrounding continents. We observe a decrease in the variability of the Tropical Atlantic after 1970 in both CMIP6 models and observations. Most of the Tropical Atlantic interannual variability is explained by its equatorial (Atlantic Zonal Mode, AZM) and meridional (Atlantic Meridional Mode, AMM) modes of variability. The observed wind relaxation after 1970 in both the equatorial and Tropical North Atlantic (TNA) plays a role in the decreased variability. Concerning the AZM, a widespread warming trend is observed in the equatorial Atlantic accompanied by a weakening trend of the trade winds. This drives a weakening in the Bjerknes Feedback by deepening the thermocline in the eastern equatorial Atlantic and increasing the thermal damping. Even though individually the TNA and Tropical South Atlantic (TSA) show increased variability, the observed asymmetric warming in the Tropical Atlantic and relaxed northeast trade winds after the 70s play a role in decreasing the AMM variability. This configuration leads to positive Wind-Evaporation-SST (WES) feedback, increasing further the TNA SST, preventing AMM from changing phases as before 1970. Associated with it, the African Sahel shows a positive precipitation trend and the Intertropical Convergence Zone tends to shift northward, which acts on maintaining the increased precipitation.

Adiabatic and diabatic signatures of ocean temperature variability

Anthropogenically induced radiative imbalances in the climate system lead to a slow accumulation of heat in the ocean. This warming is often obscured by natural modes of climate variability such as the El Niño-Southern Oscillation (ENSO), which drive substantial ocean temperature changes as a function of depth and latitude. The use of watermass coordinates has been proposed to help isolate forced signals and filter out fast adiabatic processes associated with modes of variability. However, how much natural modes of variability project into these different coordinate systems has not been quantified. Here we apply a rigorous framework to quantify ocean temperature variability using both a quasi-Lagrangian, watermass-based temperature coordinate and Eulerian depth and latitude coordinates in a free-running climate model under pre-industrial conditions. The temperature-based coordinate removes the adiabatic component of ENSO-dominated interannual variability by definition, but a substantial diabatic signal remains. At slower (decadal to centennial) frequencies, variability in the temperature- and depth-based coordinates is comparable. Spectral analysis of temperature tendencies reveals the dominance of advective processes in latitude and depth coordinates while the variability in temperature coordinates is related closely to the surface forcing. Diabatic mixing processes play an important role at slower frequencies where quasi steady-state balances emerge between forcing and mixing in temperature, advection and mixing in depth, and forcing and advection in latitude. While watermass-based analyses highlight diabatic effects by removing adiabatic variability, our work shows that natural variability has a strong diabatic component and cannot be ignored in the analysis of long term trends.

Late Holocene Hydro-Climate Variability in the Eastern Mediterranean: A Spatial Multi-Proxy Approach

A total of thirteen (13) paleoclimatic coastal and hinterland archives of the broader eastern Mediterranean region were collected and examined statistically in search of underlying trends for the period 2800 to 200 BP. For each archive, a proxy record representative of hydro-climatic changes was selected, normalized using z-factors to facilitate intercomparison, and analyzed statistically. Multivariate statistical analysis was performed using a clustering analysis (HCA) and dimension reduction (PCA), which led to groupings of similar records temporally, and allowed the identification of spatially underlying modes of variability. Two main modes of variability were identified, further supporting complex trajectories of paleoclimatic evolution in the region. The first mode was identified for sites presenting a trend from a wetter to an overall drier phase, with respective changes at major phase shifts at 1400 BP and 1100 BP. All sites were from the southern and northern Balkan region, as well as southwestern Turkey. A contrasting dry to wet trend was identified for a site in the Peloponnese (Greece) and the Levant, with a major phase shift at around 750 BP. The inclusion of different proxies from very different environmental settings and the 200-year window has complicated the connection of established short-term climatic events to the study’s findings.

Wavelet Analysis of Rainfall and Runoff Multidecadal Time Series on Large River Basins in Western North Africa

This work is dedicated to the study of the spatio-temporal variability of climate in Morocco by the analysis of rainfall (gridded and gauged data) and runoff. The wavelet analysis method has been used in this study to compare the rainfall and runoff series and to show the major discontinuities identified in 1970, 1980, and 2000. Several modes of variability have been detected; this approach has been applied to show annual (1 year) and inter-annual modes (2–4 years, 4–8 years, 8–12/8–16 years, and 16–30 years), and some modes are specific to some stations. This analysis will be complemented by the gridded data covering the period from 1940 to 1999, which will allow for a better understanding of the spatial variability of the highlighted signals set, which identified frequencies at 1 year and 8–16 years, distinguished different time periods at each basin and identified three main discontinuities in 1970, 1980, and 2000. The contribution of climatic indices is important as it is between 55% and 80%.

Spectral analysis of climate dynamics with operator-theoretic approaches

The Earth’s climate system is a classical example of a multiscale, multiphysics dynamical system with an extremely large number of active degrees of freedom, exhibiting variability on scales ranging from micrometers and seconds in cloud microphysics, to thousands of kilometers and centuries in ocean dynamics. Yet, despite this dynamical complexity, climate dynamics is known to exhibit coherent modes of variability. A primary example is the El Niño Southern Oscillation (ENSO), the dominant mode of interannual (3–5 yr) variability in the climate system. The objective and robust characterization of this and other important phenomena presents a long-standing challenge in Earth system science, the resolution of which would lead to improved scientific understanding and prediction of climate dynamics, as well as assessment of their impacts on human and natural systems. Here, we show that the spectral theory of dynamical systems, combined with techniques from data science, provides an effective means for extracting coherent modes of climate variability from high-dimensional model and observational data, requiring no frequency prefiltering, but recovering multiple timescales and their interactions. Lifecycle composites of ENSO are shown to improve upon results from conventional indices in terms of dynamical consistency and physical interpretability. In addition, the role of combination modes between ENSO and the annual cycle in ENSO diversity is elucidated.

Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model

In this study, we analyze extreme daily precipitation during the pre-industrial period from 1501 BCE to 1849 CE in simulations from the Community Earth System Model version 1.2.2. A peak-over-threshold (POT) extreme value analysis is employed to examine characteristics of extreme precipitation and to identify connections of extreme precipitation with the external forcing and with modes of internal variability. The POT analysis shows that extreme precipitation with similar statistical characteristics, i.e., the probability density distributions, tends to cluster spatially. There are differences in the distribution of extreme precipitation between the Pacific and Atlantic sectors and between the northern high and southern low latitudes. Extreme precipitation during the pre-industrial period is largely influenced by modes of internal variability, such as El Niño–Southern Oscillation (ENSO), the Pacific North American, and Pacific South American patterns, among others, and regional surface temperatures. In general, the modes of variability exhibit a statistically significant connection to extreme precipitation in the vicinity to their regions of action. The exception is ENSO, which shows more widespread influence on extreme precipitation across the Earth. In addition, the regions with which extreme precipitation is more associated, either by a mode of variability or by the regional surface temperature, are distinguished. Regional surface temperatures are associated with extreme precipitation over lands at the extratropical latitudes and over the tropical oceans. In other regions, the influence of modes of variability is still dominant. Effects of the changes in the orbital parameters on extreme precipitation are rather weak compared to those of the modes of internal variability and of the regional surface temperatures. Still, some regions in central Africa, southern Asia, and the tropical Atlantic ocean show statistically significant connections between extreme precipitation and orbital forcing, implying that in these regions, extreme precipitation has increased linearly during the 3351-year pre-industrial period. Tropical volcanic eruptions affect extreme precipitation more clearly in the short term up to a few years, altering both the intensity and frequency of extreme precipitation. However, more apparent changes are found in the frequency than the intensity of extreme precipitation. After eruptions, the return periods of extreme precipitation increase over the extratropical regions and the tropical Pacific, while a decrease is found in other regions. The post-eruption changes in the frequency of extreme precipitation are associated with ENSO, which itself is influenced by tropical eruptions. Overall, the results show that climate simulations are useful to complement the information on pre-industrial extreme precipitation, as they elucidate statistical characteristics and long-term connections of extreme events with natural variability.

Seasonal forecasts of the Saharan heat low characteristics: a multi-model assessment

The Saharan heat low (SHL) is a key component of the West African Monsoon system at the synoptic scale and a driver of summertime precipitation over the Sahel region. Therefore, accurate seasonal precipitation forecasts rely in part on a proper representation of the SHL characteristics in seasonal forecast models. This is investigated using the latest versions of two seasonal forecast systems namely the SEAS5 and MF7 systems from the European Center of Medium-Range Weather Forecasts (ECMWF) and Météo-France respectively. The SHL characteristics in the seasonal forecast models are assessed based on a comparison with the fifth ECMWF Reanalysis (ERA5) for the period 1993–2016. The analysis of the modes of variability shows that the seasonal forecast models have issues with the timing and the intensity of the SHL pulsations when compared to ERA5. SEAS5 and MF7 show a cool bias centered on the Sahara and a warm bias located in the eastern part of the Sahara respectively. Both models tend to underestimate the interannual variability in the SHL. Large discrepancies are found in the representation of extremes SHL events in the seasonal forecast models. These results are not linked to our choice of ERA5 as a reference, for we show robust coherence and high correlation between ERA5 and the Modern-Era Retrospective analysis for Research and Applications (MERRA). The use of statistical bias correction methods significantly reduces the bias in the seasonal forecast models and improves the yearly distribution of the SHL and the forecast scores. The results highlight the capacity of the models to represent the intraseasonal pulsations (the so-called east–west phases) of the SHL. We notice an overestimation of the occurrence of the SHL east phases in the models (SEAS5, MF7), while the SHL west phases are much better represented in MF7. In spite of an improvement in prediction score, the SHL-related forecast skills of the seasonal forecast models remain weak for specific variations for lead times beyond 1 month, requiring some adaptations. Moreover, the models show predictive skills at an intraseasonal timescale for shorter lead times.