Spatiotemporal Variations of Water Vapor Content and Its Relationship with Meteorological Elements in the Third Pole

Water vapor content plays an important role in climate change and the ecosystem in the Tibetan Plateau (TP) through its complicated interaction with the meteorological elements. However, due to the complex topography of the Tibetan Plateau, it is unreliable to attempt to understand the variation pattern of water vapor content using only observational data. Satellite and reanalysis data can be a good substitute for observational data, but their accuracy still needs to be evaluated. Therefore, based on radiosonde stations data, comprehensive assessment of water vapor content on the TP and surrounding areas derived from ERA-5, Second Modern-Era Retrospective analysis for Research and Applications (MERRA2), Atmospheric Infrared Sounder (AIRS)-only, and weighted ensemble data was performed in the context of spatial and temporal distribution at the annual and seasonal scale. Based on precipitation from Gauge V3.0 and Tropical Rainfall Measuring Mission satellite (TRMM) and temperature from ERA-5, the relationship between water vapor content and temperature and precipitation was analyzed. The results show that water vapor content decreases from southeast to northwest, and ERA-5, MERRA2, and AIRS-only can reasonably reproduce the spatial distribution of annual and seasonal water vapor content, with ERA-5 being more reliable in reproducing the spatial distribution. Over the past 50 years, the water vapor content has shown a gradual increasing trend. The variation trends of AIRS-only, MERRA2, ERA-5, and weighted ensemble data are almost consistent with the radiosonde stations data, with MERRA2 being more reliable in capturing water vapor content over time. Weighted ensemble data is more capable of capturing water vapor content characteristics than simple unweighted products. The empirical orthogonal function (EOF) analysis shows that the first spatial mode values of water vapor content and temperature are positive over the TP, while the values of precipitation present a “negative-positive-negative” distribution from south to north over the TP. In the second spatial mode of EOF analysis, the values of water vapor content, air temperature, and precipitation are all negative. The first temporal modes of EOF analysis, water vapor content, air temperature, and precipitation all show an increasing trend. In conclusion, there is a clear relationship of water vapor content with temperature and precipitation.

Application of S_TIDE in exploration of seasonal variations of internal tidal amplitudes in the northern South China Sea

An enhanced harmonic analysis (S_TIDE) approach is adopted to examine the seasonal variations of internal tidal amplitudes in the northern South China Sea (SCS). Results of idealized experiments reveal that the seasonality can be captured by S_TIDE. By applying S_TIDE to mooring data, observed seasonality of internal tidal amplitudes in the northern SCS are explored. Not diurnal and semidiurnal internal tides (ITs), but overtides and long-period constituents of ITs exhibit clear seasonal cycles. However, differences between amplitudes of the eastward velocity and the northward counterpart are evident for K1, M2 and MK3, which may be caused by the intensification of background currents. Amplitudes of those ITs are stronger at intersection time between spring and summer in the eastward direction, but weaker in the northward direction. EOF analysis reveals that modes of diurnal ITs are higher than those of seimidiurnal ITs, which induces relatively more complicated seasonal variations. In addition to intensification of background currents, influences of surface tides and stratification will also induce variations of internal tidal amplitudes, introducing tremendous difficulty in predicting variation trends of internal tidal amplitudes, which greatly reduces predictability of ITs.

Intraseasonal Sea-Level Variability in the Persian Gulf

Satellite observations are used to establish the dominant magnitudes, scales, and mechanisms of intraseasonal variability in ocean dynamic sea level () in the Persian Gulf over 2002–2015. Empirical orthogonal function (EOF) analysis applied to altimetry data reveals a basin-wide, single-signed intraseasonal fluctuation that contributes importantly to variance in the Persian Gulf at monthly to decadal timescales. An EOF analysis of Gravity Recovery and Climate Experiment (GRACE) observations over the same period returns a similar large-scale mode of intraseasonal variability, suggesting that the basin-wide intraseasonal variation has a predominantly barotropic nature. A linear barotropic theory is developed to interpret the data. The theory represents Persian-Gulf-average () in terms of local freshwater flux, barometric pressure, and wind stress forcing, as well as at the boundary in the Gulf of Oman. The theory is tested using a multiple linear regression with these freshwater flux, barometric pressure, wind stress, and boundary quantities as input, and as output. The regression explains 70%±79% (95% confidence interval) of the intraseasonal variance. Numerical values of regression coefficients computed empirically from the data are consistent with theoretical expectations from first principles. Results point to a substantial non-isostatic response to surface loading. The Gulf of Oman boundary condition shows lagged correlation with upstream along the Indian Subcontinent, Maritime Continent, and equatorial Indian Ocean, suggesting a large-scale Indian-Ocean influence on intraseasonal variation mediated by coastal and equatorial waves, and hinting at potential predictability. This study highlights the value of GRACE for understanding sea level in an understudied marginal sea.

Climatology and variability of cyclone clustering

<div> <div> <div> <p>The existence of cyclone clustering, the succession of multiple extratropical cyclones during a short period of time, indicates that the baroclinicity feeding these storms undergoes longer lasting episodic cycles supporting multiple cyclones. However, the generally accepted paradigm for baroclinic instability implies that individual cyclones reduce baroclinicity to support their growth. This apparent contradiction motivates our hypothesis that some cyclones within increase baroclinicity, yielding a pathway for cyclone clustering. A case study of the extreme storm Dagmar confirms that a particular sequence of storms culminating in a severe cyclone is due to the fact that the previous storms act to maintain or increase the background baroclinity along which the succeeding storms evolved. </p> <p>Using a new cyclone clustering diagnostic based on spatio-temporal distance between cyclone tracks, we analyse cyclone clustering globally for the period 1979 until 2016. We complement this analysis with a baroclinicity diagnostic based on the slope of isentropic surfaces. With the isentropic slope and its tendencies, the relative roles of diabatic and adiabatic effects associated with extra-tropical cyclones in maintaining baroclinicity are assessed. We present a climatological analysis of where and when cyclone clustering occurs. We compare these findings to composites of clustered and non-clustered cyclones to quantify how consistent the proposed clustering mechanism is and its relation to changes in the frequency of atmospheric rivers. We complement this with an EOF analysis to investigate the variability of the clusters and how it covaries with the jet and diabatic heating.</p> </div> </div> </div>

EOF Analysis of Green Cumulus Mesoscale Organization

<p>Warm convective clouds play a key role in the Earth’s radiative and water budgets. Nonetheless, they still comprise the largest source of uncertainty in climate model’s prediction of cloud feedback and climate sensitivity. The latter might be affected by the variety of patterns that warm convective clouds form on the mesoscale, an effect which is largely uninvestigated, and even more so over land. A large subset of continental shallow convective cumulus (Cu) fields was shown to have unique spatial properties and to form mostly over forests and vegetated areas thus referred to as green Cu. Green Cu fields form organized mesoscale patterns, yet the underlying mechanisms, as well as the time variability of these patterns, are still lacking understanding.  In this work, we characterize the organization of green Cu in space and time, by using data-driven organization metrics, and by decomposing the high-resolution GOES–16 data using an Empirical Orthogonal Function (EOF) analysis. We extract and quantify modes of organization present in a green Cu field, during the course of a day. The EOF decomposition shows the field's key organization features such as cloud streets, and it also reveals hidden ones, as the propagation of gravity waves (GW), and the development of a highly ordered grid of clouds that extends over hundreds of kilometers, over a time span that scales as the field's lifetime. We then use cloud fields that were reconstructed from different subgroups of modes to quantify the cloud street's wavelength and aspect ratio, as well as the GW dominant period.</p>

South Adriatic Recipes: Estimating the Vertical Mixing in the Deep Pit

<p>The available historical oxygen data show that the deepest part of the South Adriatic Pit remains well-ventilated despite the winter convection reaching only the upper 700 m depth. Here, we show that the evolution of the vertical temperature structure in the deep South Adriatic Pit (dSAP) below the Otranto Strait sill depth (780 m) is described well by continuous diffusion, a continuous forcing by heat fluxes at the upper boundary (Otranto Strait sill depth) and an intermittent forcing by rare (several per decade) deep convective and gravity-current events. The analysis is based on two types of data: (i) 13-year observational data time series (2006–2019) at 750, 900, 1,000, and 1,200 m depths of the temperature from the E2M3A Observatory and (ii) 55 vertical profiles (1985–2019) in the dSAP. The analytical solution of the gravest mode of the heat equation compares well to the temperature profiles, and the numerical integration of the resulting forced heat equation compares favorably to the temporal evolution of the time-series data. The vertical mixing coefficient is obtained with three independent methods. The first is based on a best fit of the long-term evolution by the numerical diffusion-injection model to the 13-year temperature time series in the dSAP. The second is obtained by short-time (daily) turbulent fluctuations and a Prandtl mixing length approximation. The third is based on the zero and first modes of an Empirical Orthogonal Function (EOF) analysis of the time series between 2014 and 2019. All three methods are compared, and a diffusivity of approximately κ = 5 · 10<sup>−4</sup>m<sup>2</sup>s<sup>−1</sup> is obtained. The eigenmodes of the homogeneous heat equation subject to the present boundary conditions are sine functions. It is shown that the gravest mode typically explains 99.5% of the vertical temperature variability (the first three modes typically explain 99.85%) of the vertical temperature profiles at 1 m resolution. The longest time scale of the dissipative dynamics in the dSAP, associated with the gravest mode, is found to be approximately 5 years. The first mode of the EOF analysis (85%) represents constant heating over the entire depth, and the zero mode is close to the parabolic profile predicted by the heat equation for such forcing. It is shown that the temperature structure is governed by continuous warming at the sill depth and deep convection and gravity current events play less important roles. The simple model presented here allows evaluation of the response of the temperature in the dSAP to different forcings derived from climate change scenarios, as well as feedback on the dynamics in the Adriatic and the Mediterranean Sea.</p>

Evaluation of CMIP5 Climate Models Using Historical Surface Air Temperatures in Central Asia

Using historical data compiled by the Climate Research Unit, spatial and temporal analysis, trend analysis, empirical orthogonal function (EOF) analysis, and Taylor diagram analysis were applied to test the ability of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models to accurately simulate the annual mean surface air temperature in central Asia from the perspective of the average climate state and climate variability. Results show that each model can reasonably capture the spatial distribution characteristics of the surface air temperature in central Asia but cannot accurately describe the regional details of climate change impacts. Some of the studied models, including CNRM-CM5, GFDL-CM3, and GISS-E2-H, could better simulate the high- and low-value centers and the contour distribution of the surface air temperature. Taylor diagram analysis showed that the root mean square errors of all models were less than 3, the standard deviations were between 8.36 and 13.45, and the spatial correlation coefficients were greater than 0.96. EOF analysis showed that the multi-model ensemble can accurately reproduce the surface air temperature characteristics in central Asia from 1901 to 2005, including the rising periods and the fluctuations of the north and south inversion phases. Overall, this study provides a valuable reference for future climate prediction studies in central Asia.

Deciphering Organization of GOES–16 Green Cumulus, through the EOF Lens

A subset of continental shallow convective Cumulus (Cu) cloud fields were shown to have unique spatial properties and to form mostly over forests and vegetated areas, thus referred to as green Cu. Green Cu fields are known to form organized mesoscale patterns, yet the underlying mechanisms as well as the time variability of these patterns are still lacking understanding. Here, we characterize the organization of green Cu in space and time, by using data driven organization metrics, and by applying an Empirical Orthogonal Function (EOF) analysis to a high-resolution GOES–16 dataset. We extract, quantify and reveal modes of organization present in a green Cu field, during the course of a day. The EOF decomposition is able to show the field's key organization features such as cloud streets, and it also delineates the less visible ones, as the propagation of gravity waves (GW), and the emergence of a highly organized grid on a spatial scale of hundreds of kilometers, over a time period that scales with the field's lifetime. Using cloud fields that were reconstructed from different subgroups of modes, we quantify the cloud street's wavelength and aspect ratio, as well as the GW dominant period.