Empirical Orthogonal Function Research Articles

Evaluating the East Asian summer precipitation from the perspective of dominant intermodel spread modes and its implication for future projection

AbstractIn this study, a new skill score (SS) is proposed to evaluate the performance of climatological East Asian summer precipitation (EASP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the historical period. By applying the empirical orthogonal function (EOF) to the EASP bias of CMIP6 models, the intermodel spread of EASP bias is revealed to be dominated by the first two modes: the uniform precipitation bias pattern and the north–south dipole precipitation bias pattern. Then the SS is constructed by the weighted‐average model‐observation distances regarding different EOF modes, where the model‐observation distance in a certain EOF mode is defined as the difference between their principal components, and the weight is the corresponding percentage variance. The perfect‐models ensemble based on the SS shows a spatial magnitude close to the observation, indicating that the SS effectively depicts the models' historical performance. However, no robust relationship is found between the model's historical performance and future projection regarding the EASP. This is because they are governed by different physical factors. The historical EASM is determined by the thermal responses to a specific radiative forcing, while the future change in EASP is associated with the warming rate along with the increased radiative forcing.

Improving short-term, near-surface temperature forecasts by integrating weather pattern information into Model Output Statistics

Abstract Dynamical numerical weather prediction has remarkably improved over the last decades. Yet, postprocessing techniques are needed to calibrate forecasts which are based on statistical and Machine Learning techniques. With recent advances in the derivation of year-round, large-scale atmospheric circulations, or weather regimes, the question arises of whether this information can be valuable within forecast postprocessing methods. This paper investigates this by proposing a bias correction scheme to integrate the atmospheric circulation state derived from empirical orthogonal functions, referred to as weather patterns, for deterministic short-term, near-surface temperature forecasts based on LASSO regression. We propose a computational study which first evaluates different weather pattern definitions (spatial domain) to improve temperature forecasts in Europe. As a bias could be associated with the weather pattern at the model initialization time or at the realization time of the forecast, both variants are tested in this study. We show that forecasted weather patterns with the identical spatial domain as the forecast show best skill reaching Mean Squared Error Skill improvements of up to 3% (day-ahead) or 1% respectively (week ahead). Only considering land surface improvements in Europe, improvements of 4-6% for day-ahead and 1 to 5% for week-ahead forecasts are observable. We believe that this study not only introduces a simple yet effective tool to reduce bias in temperature forecasts but also contributes to the active discussion of how valuable weather patterns are and how to use them within forecast calibration techniques.

Assessment of Rainfall and Temperature Trends in the Yellow River Basin, China from 2023 to 2100

China’s Yellow River Basin (YRB) is sensitive to climate change due to its delicate ecosystem and complex geography. Water scarcity, soil erosion, and desertification are major challenges. To mitigate the YRB’s ecological difficulties, climate change must be predicted. Based on the analysis of the evolution features of hydro-meteorological elements, the CMIP6 climate model dataset with Delta downscaling and the Empirical Orthogonal Function (EOF) is utilized to quantitatively explore the future variations in precipitation and temperature in the YRB. The following results are drawn: The spatial resolution of the CMIP6 climate model is less than 0.5° × 0.5° (i.e., about 55 km × 55 km), which is improved to 1 km × 1 km by the downscaling of Delta and has outstanding applicability to precipitation and temperature in the YRB. The most accurate models for monthly mean temperature are CESM2-WACCM, NorESM2-LM, and ACCESS-CM2, and for precipitation are ACCESS-ESM1-5, CESM2-WACCM, and IPSL-CM6A-LR. Between 2023 and 2100, annual precipitation increases by 6.89, 5.31, 7.02, and 10.18 mm/10a under the ssp126, ssp245, ssp370, and ssp585 climate scenarios, respectively, with considerable variability in precipitation in the YRB. The annual temperature shows a significant upward trend, and the change rates under the different climate scenarios are, respectively, 0.1 °C/10a, 0.3 °C/10a, 0.5 °C/10a, and 0.7 °C/10a. The increase is positively correlated with emission intensity. Based on the EOF analysis, temperature and precipitation mainly exhibit a consistent regional trend from 2023 to 2100, with the primary modal EOF1 of precipitation for each scenario exhibiting a clear spatial distribution in the southeast–northwest.

Investigation of the Historical Trends and Variability of Rainfall Patterns during the March–May Season in Rwanda

This study explores the spatiotemporal variability and determinants of rainfall patterns during the March to May (MAM) season in Rwanda, incorporating an analysis of teleconnections with oceanic–atmospheric indices over the period 1983–2021. Utilizing the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset, the study employs a set of statistical tools including standardized anomalies, empirical orthogonal functions (EOF), Pearson correlation, the Mann–Kendall (MK) trend test, and Sen’s slope estimator to dissect the intricacies of rainfall variability, trends, and their association with large-scale climatic drivers. The findings reveal a distinct southwest to northwest rainfall gradient across Rwanda, with the MK test signaling a decline in annual precipitation, particularly in the southwest. The analysis for the MAM season reveals a general downtrend in rainfall, attributed in part to teleconnections with the Indian Ocean Sea surface temperatures (SSTs). Notably, the leading EOF mode for MAM rainfall demonstrates a unimodal pattern, explaining a significant 51.19% of total variance, and underscoring the pivotal role of atmospheric dynamics and moisture conveyance in shaping seasonal rainfall. The spatial correlation analysis suggests a modest linkage between MAM rainfall and the Indian Ocean Dipole, indicating that negative (positive) phases are likely to result in anomalously wet (dry) conditions in Rwanda. This comprehensive assessment highlights the intricate interplay between local rainfall patterns and global climatic phenomena, offering valuable insights into the meteorological underpinnings of rainfall variability during Rwanda’s critical MAM season.

Satellite-Based Estimation of Near-Surface NO2 Concentration in Cloudy and Rainy Areas

Satellite-based remote sensing enables the quantification of tropospheric NO2 concentrations, offering insights into their environmental and health impacts. However, remote sensing measurements are often impeded by extensive cloud cover and precipitation. The scarcity of valid NO2 observations in such meteorological conditions increases data gaps and thus hinders accurate characterization and variability of concentration across geographical regions. This study utilizes the Empirical Orthogonal Function interpolation in conjunction with the Extreme Gradient Boosting (XGBoost) algorithm and dense urban atmospheric observed station data to reconstruct continuous daily tropospheric NO2 column concentration data in cloudy and rainy areas and thereby improve the accuracy of NO2 concentration mapping in meteorologically obscured regions. Using Chengdu City as a case study, multiple datasets from satellite observations (TROPOspheric Monitoring Instrument, TROPOMI), near-surface NO2 measurements, meteorology, and ancillary data are leveraged to train models. The results showed that the integration of reconstructed satellite observations with provincial and municipal control surface measurements enables the XGBoost model to achieve heightened predictive accuracy (R2 = 0.87) and precision (RMSE = 5.36 μg/m3). Spatially, this approach effectively mitigates the problem of missing values in estimation results due to absent satellite data while simultaneously ensuring increased consistency with ground monitoring station data, yielding images with more continuous and refined details. These results underscore the potential for reconstructing satellite remote sensing information and combining it with dense ground observations to greatly improve NO2 mapping in cloudy and rainy areas.

Reconstructing the sound speed profile of South China Sea using remote sensing data and long short-term memory neural networks

IntroductionSound waves are refracted along the direction of their propagation owing to spatial and temporal fluctuations in the speed of sound in seawater. Errors are compounded when sound speed profiles (SSPs) with low precision are used to detect and locate distant underwater targets because an accurate SSP is critical for the identification of underwater objects based on acoustic data. Only sparse historical spatiotemporal data on the SSP of the South China Sea are available owing to political issues, its complex atmospheric system, and the unique topography of its seabed, because of which frequent oceanic movements at the mesoscale affect the accuracy of inversion of its SSP.MethodIn this study, we propose a method for the inversion of the SSP of the South China Sea based on a long short-term memory model. We use continuous-time data on the SSP of the South China Sea as well as satellite observations of the height and temperature of the sea surface to make use of the long-term and short-term memory-related capacities of the proposed model.ResultIt can achieve highly accurate results while using a small number of samples by virtue of the unique structure of its memory. Compared with the single empirical orthogonal function regression method, the inversion accuracy of this model is improved by 24.5%, and it performed exceptionally well in regions with frequent mesoscale movements.DiscussionThis enables it to effectively address the challenges posed by the sparse sample distribution and the frequent mesoscale movements of the South China Sea.

Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta

Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta

Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan

Satellite sea surface temperature (SST) images are valuable for various oceanic applications, including climate monitoring, ocean modeling, and marine ecology. However, cloud cover often obscures SST signals, creating gaps in the data that reduce resolution and hinder spatiotemporal analysis, particularly in the waters near Taiwan. Thus, gap-filling methods are crucial for reconstructing missing SST values to provide continuous and consistent data. This study introduces a gap-filling approach using the Double U-Net, a deep neural network model, pretrained on a diverse dataset of Level-4 SST images. These gap-free products are generated by blending satellite observations with numerical models and in situ measurements. The Double U-Net model excels in capturing SST dynamics and detailed spatial patterns, offering sharper representations of ocean current-induced SST patterns than the interpolated outputs of Data Interpolating Empirical Orthogonal Functions (DINEOFs). Comparative analysis with buoy observations shows the Double U-Net model’s enhanced accuracy, with better correlation results and lower error values across most study areas. By analyzing SST at five key locations near Taiwan, the research highlights the Double U-Net’s potential for high-resolution SST reconstruction, thus enhancing our understanding of ocean temperature dynamics. Based on this method, we can combine more high-resolution satellite data in the future to improve the data-filling model and apply it to marine geographic information science.

Atmospheric teleconnections responsible for the dominant patterns of interannual variability in summer drought over northern Asia

Abstract Summer drought over northern Asia (NA) seriously threatens the local fragile ecological environment and social economy development. In this study, using the standardized precipitation evapotranspiration index (SPEI), we firstly identify the dominant modes of interannual variability in summer drought condition over NA, and then explore the atmospheric patterns responsible for the formation of the modes. The results show that the first empirical orthogonal function mode (EOF1) of summer SPEI over NA exhibits a meridional dipole pattern, which is influenced primarily by the Polar–Eurasian teleconnection (POL) and Circumglobal teleconnection (CGT) patterns. Under the influence of negative POL and positive CGT patterns, there is an anomalous anticyclone (cyclone) over northwestern Siberia (Lake Baikal to Northeast China). Such atmospheric circulations lead to meridional dipole patterns in air temperature, moisture condition, vertical motion, and cloud cover over NA, favoring decreased (increased) precipitation and increased (decreased) potential evapotranspiration over northern (southern) NA, finally contributing to the formation of EOF1. The EOF2 shows an approximate zonal dipole pattern, which is influenced by the British-Baikal Corridor (BBC) and Scandinavia teleconnection (SCA) patterns. The positive BBC and SCA patterns can lead to an anomalous anticyclone over the Ural Mountains and cyclone over the Lake Baikal. Such atmospheric circulations result in a zonal dipole pattern in precipitation and potential evapotranspiration over NA through changing the local moisture condition, air temperature, and radiation, consequently favoring the formation of EOF2. Fitting analysis indicates that the aforementioned atmospheric factors can explain 76% (55%) of the interannual variability of EOF1 (EOF2).

Asymmetric responses of summer soil moisture anomalies to the large-scale teleconnections on the Loess Plateau between 2000 and 2020

Soil moisture is a key regulator of hydrological cycle and is closely related to vegetation growth and climate changes, which is mainly replenished by summer precipitation on the Loess Plateau. However, the response of soil moisture content (SMC) anomalies during summer on the Loess Plateau to large-scale teleconnection remains unclear. Here, using the empirical orthogonal function (EOF) method, this study extracted a homogenized area with significant variability of normalized difference vegetation index (NDVI) (p<0.01), which was called sensitive areas for vegetation restoration (SAVR) of the Loess Plateau. Based on 1 km daily soil moisture dataset named SMCI1.0 at top 1 m, we demonstrated that there was an interannual fluctuation of summer SMC from 2000 to 2020 over the SAVR of the Loess Plateau. Spatially, SMC during summer in the SAVR of the Loess Plateau exhibited an enhanced regional difference characterized by ‘dry to drier and wet to wetter’. Importantly, abnormal SMC during summer in the SAVR of the Loess Plateau can been attributed to distinct circulation pattern and anomalous water vapor transport, which showed asymmetrical distributions between positive and negative anomalous years of summer SMC. Due to the activities of western Pacific subtropical high (WPSH) and low-pressure system around the Lake Baikal (an anomalous anticyclone prevailed over the western north Pacific), the positive (negative) anomalous years of summer SMC witnessed net water vapor influx (outflux) anomalies to the Loess Plateau compared to the climatology, corresponding to anomalous upward (sinking) motions, further leading to more (less) SMC in summer over the SAVR of the Loess Plateau, respectively. The conclusions of this study have implications for excavating early warning information of SMC anomalies and natural restoration sustainable management on the Loess Plateau.

Investigating a novel feature of multi-phase rotated empirical orthogonal function to capture spatiotemporal temperature variations

Investigating a novel feature of multi-phase rotated empirical orthogonal function to capture spatiotemporal temperature variations

Trend and interannual variability of summer marine heatwaves in the tropical Indian ocean: Patterns, mixed layer heat budget, and seasonal prediction

Marine heatwaves (MHWs) are extreme sea surface temperature (SST) events in all ocean basins, with far-reaching impacts on marine ecosystems and socio-economy. The leading patterns, trend, and interannual variability of summer MHWs in the tropical Indian Ocean (TIO) are investigated in this study. The first empirical orthogonal function (EOF) mode of frequency of MHWs exhibits a monopole pattern over the entire basin. This mode is highly associated with the concurrent Indian Ocean Basin warming, indicating a remarkable trend over the past four decades. The linear trend in MHW properties largely relates to increased summer Indian Ocean mean SST. The second EOF mode exhibits a zonal dipole with the MHW numbers increasing in the west and decreasing in the east. On the interannual timescale, the first two EOF modes are remotely affected by antecedent and concurrent El Niño events, respectively. The ocean mixed layer budget is utilized for examining the formation of different summer MHW patterns. During the preceding spring, the surface heat flux is important for the development of MHWs, while the ocean advections play a secondary role in the South Indian Ocean for the MHW monopole. Once the SST anomaly rises in summer, the ocean advections play a dominant role in maintaining the SST. Last, we assess the prediction skill of summer TIO MHWs by performing a bilinear seasonal statistical prediction model. Our results suggest the frequency of summer MHWs in the TIO could be predicted one season in advance. This study has great implications for understanding and predicting ocean extreme events in the TIO.

Analysis of sea level variability and its contributions in the Bohai, Yellow Sea, and East China Sea

Understanding the sea level variability of the Bohai, Yellow Sea, and East China Sea (BYECS) is crucial for the socio-cultural and natural ecosystems of the coastal regions. In this study, based on satellite altimetry data, selected time range from 1993 to 2020, using the cyclostationary empirical orthogonal function (CSEOF) analysis method distinguishes the primary sea level variability modes. The analysis encompasses the seasonal signal, trend, and El Niño-Southern Oscillation (ENSO) associated mode of sea level anomaly. The amplitude of the annual cycle demonstrates a non-stationary signal, fluctuating between -15% and 15% from the average. Monsoons, atmospheric forcing, ocean circulation, wind-driven Ekman transport, and the Kuroshio emerge as the primary factors influencing BYECS variability on seasonal scales. The satellite altimetry sea level exhibits an average trend within the range of 3-4 mm/year, while the steric sea level trend is generally smaller, falling within the range of 0-2 mm/year. Throughout the entire period, the contribution of steric sea level to the mean sea level trend consistently remains below 25%. Furthermore, BYECS sea level variations have a sensitive response to strong El Niño years, with a clear regionalization of the response, which is related to the intricate atmospheric circulation and local wind pressures, as well as the influence of ocean circulation. In conclusion, we gained a more comprehensive understanding of sea level variability in the BYECS, especially the annual cycle of sea level amplitude and the response of ENSO. However, more studies still need to be done to differentiate the various factors in sea level variations.

Estimation of groundwater storage variations in African river basins: Response to global climate change using GRACE and GRACE-FO among past two decades

Groundwater Storage (GWS) is an important component of the water cycle and a vital resource for supplying water to millions of people as well as sustaining domestic and economic activities, especially in arid and semi-arid regions such as Africa. This study aims to estimate and analyze the recent groundwater changes (ΔGW) for 11 major African basins in light of climate changes and global warming utilizing the data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) from January 2003 to December 2021. We proposed a new gap-filling technique to calculate the missing months between GRACE and GRACE-FO missions for the continuous estimation of groundwater changes. Then, we establish a new framework to analyze and investigated the climate parameters that could affect groundwater changes using Empirical Orthogonal Function (EOF) technique. Our analysis reveals that the new gap-filling technique is significantly consistent with the TWS time-series based on the trend line analysis. The lowest GPCC drought index value occurs in the lower Nile basin by approximately −0.93, while the highest occurs in the middle of Africa in Congo basin by approximately −0.02. The precipitation rate is mostly concentrated in mid-Africa while North and South Africa shows a weak rate. Meanwhile, according to the analysis of dependent climate parameters, climate change has had a negative impact on groundwater. However, the long-term effect on the groundwater does not clearly appear because most of Africa is located within semi-arid and arid regions. The effect of rain on groundwater was evident through EOF analysis, which shows GWS EOF Mode-2 (36.28%) and precipitation EOF Mode-1 (78.34%) were found to be in significant agreement in spatio-temporal domains.

Analysis on characteristics of extreme precipitation indices and atmospheric circulation in Northern Shanxi

This article utilizes daily precipitation data from 28 national meteorological stations in northern Shanxi Province spanning from 1972 to 2020, and the US NCEP/NCAR monthly average reanalysis and ERA5 monthly average reanalysis data. The study employs techniques such as empirical orthogonal function (EOF) decomposition, Mann-Kendall mutation and other methods to investigate the spatiotemporal distribution of extreme precipitation index in northern Shanxi and their correlation with atmospheric circulation. The research results show that: the absolute index, relative index, intensity index and sustained dry period index (CDD) in the continuous index appear from southwest to northeast. The spatial distribution characteristics of the central region decrease, while the continuous wet period (CWD) decreases from the central to the east and west. The three indices Rx1day, Rx5day, and CWD mutated in 1978, 1975, and 1983 respectively, and other extreme precipitation indices all appeared in a sudden change from a low-value period to a high-value period occurred around 2010. In the high-value years of the summer extreme precipitation index, there is a significant negative anomaly in the height field in the mid-high latitude regions of Eurasia. Northern Shanxi is controlled by a broad low-pressure trough in the Lake Baikal area. Water vapor transported via the east, west, and south routes converges in the northern Shanxi region and encounters cold air from the north. There is a strong upward motion anomaly at 500 hPa in the troposphere, and the dynamic conditions of upper-level divergence and lower-level convergence lead to more summer extreme precipitation in the northern Shanxi region. Conversely, in the low-value years of the summer extreme precipitation index, northern Shanxi is affected by a strong high-pressure ridge north of Lake Baikal. There is a downward motion anomaly at 500 hPa, and the northern Shanxi region lacks water vapor. The cold and warm air cannot converge, and both the water vapor conditions and dynamic conditions are poor, which is not conducive to the production of extreme precipitation in northern Shanxi.

Modes of Weather System-Induced Flows through an Arctic Lagoon

With the increasing warming of the Arctic, the summertime ice-free period in the coastal Arctic becomes longer and the water exchange between arctic lagoons and coastal Beaufort Sea becomes more important for land–ocean interaction. This study examined the dynamics of water exchange between the arctic lagoons and the Arctic Ocean under the influence of weather systems (the transient arctic cyclones and hovering Beaufort High pressure system). We implemented rare observations, numerical modeling with the Finite Volume Community Ocean Model (FVCOM), and a forcing-response Empirical Orthogonal Function (fr-EOF) analysis to determine the weather-driven flow patterns and characteristics in the micro-tidal arctic lagoon (Elson Lagoon) with little freshwater discharge. The results were validated for both tidal and subtidal currents with in situ data. The inlets of the lagoon were significantly impacted by wind associated with the weather systems and the flows through the inlets were highly correlated with each other. The fr-EOF analysis for the 1.5-month FVCOM output indicated three significant modes of wind-driven flow. In the deepest (~16 m) northwestern-most inlet, a counter-wind flow occurred more than 96% of the time due to setup and set down of water level inside the lagoon and the vorticity balance related to the wind stress and water depth. For about 60–80% of the time, the exchange flow was out of the lagoon through the northwestern-most and deepest inlet due to the strong easterly winds dictated by the Beaufort High; this dominant flow is mainly caused by the persistent easterly wind as a limb of the Beaufort High pressure system, modified by the transient arctic cyclones with a westerly wind and inward flows at the westernmost inlet of Elson Lagoon. This study shows that the alternating influence from the cyclone-anticyclone weather systems produces a meteorological tide in the subtidal spectrum which dominates the water exchange in the region through the multiple inlets. With the observed increase in cyclone strength and frequency under the warming trend, this may imply a greater contribution from the westerly wind because of the increased cyclonic activities. If this is the case, the inward flow might increase and have an effect on sediment, larval, and nutrient transports through this system.

Climate Variability of Air Temperature and Its Warming Trends in the Arabian Gulf

AbstractOver a 41-year period (1979–2019), the climate of the Arabian Gulf (Gulf) exhibited notable variability. This variation, influenced by mesoscale and globalscale meteorological and climatic determinants, showed temperature fluctuations, predominantly in the Northern Gulf (N Gulf). The N Gulf recorded an average temperature of 25.9 °C, a noticeable 3 °C higher than the broader Gulf average. This period marked extreme temperature records, with a peak at 53.8 °C in July 2016, paralleling the highest known temperatures in Asia. Data reveals a warming trend (0.1–0.6 °C per decade) in the region, with the N Gulf’s rise surpassing global trends. The years 2014–2019 were notably warmer, impacting overall trends, and 1997 was the transitional year shifting from cooler to warmer climates. The N Gulf’s seasonal dynamics are also transforming, with extended summers and shortened winters. In this study, we employed empirical orthogonal function (EOF) decomposition analysis to examine the interannual to multidecadal fluctuations in air temperature within the Gulf from 1979 to 2019, using hourly air temperature data from the Watch Forcing Dataset (WFDE5) generated from the fifth generation (ERA5) European Centre for Medium-Range Weather Fore-casts (ECMWF). Through this method, we discerned three principal EOF modes, which together accounted for 82% of the air temperature variability in the Gulf, highlighting external influences such as North Atlantic Oscillation (NAO), El Nino-Southern Oscillation (ENSO), and Atlantic Multidecadal Oscillation (AMO). The data for these external influences were sourced from the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory. These findings stress the pressing need for detailed climate studies and mitigation strategies for a rapidly warming Gulf region, especially the N Gulf.

Characteristics of the East Asian Summer Monsoon Using GK2A Satellite Data

In East Asia, where concentrated summer precipitation often leads to climate disasters, understanding the factors that cause such extreme rainfall is crucial for effective forecasting and preparedness. The western North Pacific subtropical high (WNPSH) is a key driver of summer precipitation variability, and therefore, its monitoring is critical to predicting the wet or dry periods during the East Asian summer monsoon. Using the Geo-KOMPSAT 2A (GK2A) satellite cloud amount data and ERA5 reanalysis data during the years 2020–2023, this study identified three leading empirical orthogonal function (EOF) modes and investigated the associated WNPSH variability at synoptic and subseasonal scales. The analysis includes a linear regression of meteorological fields onto the principal component (PC) time series. All three modes play a role in the spatiotemporal variability of the WNPSH, exhibiting lead–lag relationships. In particular, the second mode is responsible for its northwestward shift and intensification. As the WNPSH moves northwestward, the position of the monsoon rain band also shifts, and its intensity is modulated mainly by the moisture transport along the WNPSH boundary. Our results highlight the potential of high-resolution, real-time data from the GK2A satellite to elucidate WNPSH variability and its impact on the East Asian summer monsoon. By addressing the variability of the WNSPH using GK2A data, we pave the way for the development of a real-time monitoring framework with GK2A, which will improve our predictability and readiness for extreme weather events in East Asia.

Asian-Australian monsoon as a mediator on North American surface air temperature anomalies

The Asian-Australian monsoon (AAM) significantly shapes global weather and climate patterns, yet existing research has primarily focused on regional monsoon subsystems rather than adopting a comprehensive global perspective on the AAM. This study addresses this gap by elucidating the interannual variability of the AAM during boreal summer and underscoring the pivotal mediating role of AAM precipitation anomalies as a driving force behind surface air temperature anomalies (SATA) over central and southern North America (CSNA). To investigate AAM characteristics, we employ an empirical orthogonal function analysis to identify the primary mode of the AAM. The corresponding principal component serves as a representative measure for the AAM. Our findings reveal a distinct ‘wet (dry) Maritime Continent-dry (wet) Western North Pacific (WNP)’ dipole pattern in strong (weak) AAM years. Vertical motion anomalies emerge as the dominant contributors to these precipitation anomalies. Both observed data and model results indicate that the sea surface temperature anomalies (SSTA) in the central and eastern Pacific (CEP) play a crucial role in explaining the vertical motion anomalies. This explanation is facilitated through two key systems: the east–west Walker circulation-like anomaly and the anomalous equatorially symmetric anticyclonic pair in the Pacific. AAM precipitation anomalies act as a mediator between SSTA in the CEP and SATA over the CSNA. These generate Rossby wave source over the WNP, triggering Rossby waves that propagate to CSNA along a great circle path. This process results in upper-level positive geopotential height anomalies, thereby influencing SATA positively over the CSNA, as illustrated by the linear baroclinic model. These findings not only deepen our understanding of the AAM but also offer novel insights into the mechanisms influencing SATA formation over North America.

The dominant modes of recent sea level variability from 1993 to 2020 in the China Seas

A better understanding of internal climatic variability is essential for estimating and predicting regional sea level rise in the coming decades. Based on the satellite altimeter data, the cyclostationary empirical orthogonal function (CSEOF) analysis is used to extract and examine the seasonal signal, trend, El Niño-Southern Oscillation (ENSO)-related, and low-frequency mode of sea level anomaly in the China Seas from 1993 to 2020. The interannual changes in the annual cycle of sea level were significantly influenced by the mean surface pressure and the 10-m zonal wind component. Furthermore, strong El Niño events (1997/1998) can significantly affect seasonal fluctuations through precipitation and runoff. By recombining the loading vector and principal component time series of the trend mode, we estimate the sea level trends to be about 3.63 ± 0.37 mm/yr. The South China Sea, followed by the Yellow Sea, is where ENSO's effects on sea level fluctuations are most noticeable. The 2015–2016 El Niño, which was a combination of the Central Pacific (CP) ENSO Eastern Pacific (EP) ENSO, had average sea levels that were much lower than those of the 1997–1998 (CP ENSO) and 2009–2010 (CP ENSO). The Pacific Decadal Oscillation (PDO) can significantly affect the low frequency mode, but other sources also contribute to this mode. The South China Sea, especially to the west of Luzon Island, exhibits the highest level of variability in the low-frequency mode. Finally, our work shows that, in contrast to global or oceanic scales, regional CSEOF analysis does, in fact, capture regional sea level variability more accurately and detailed.