Empirical Orthogonal Function Decomposition Research Articles

Quantitative response relationships between annual precipitation in China from 1951 to 2018 and its influencing factors

Abstract Continuous changes in the global climate have exacerbated the uneven spatial distribution of water resources. The quantitative response relationships between precipitation and its influencing factors are an important research topic. In this study, the responses of precipitation to its influencing factors were quantified by analysing the large-spatial-scale data such as the monthly precipitation data of 619 meteorological stations in China from 1951 to 2018, the CMIP6 data, and the AMO through empirical orthogonal function decomposition, partial redundancy analysis, and ensemble empirical mode decomposition. As shown by the results, the overall response relationships between precipitation in China and the AMO and MEI were relatively strong, and the PDO and AGG affected precipitation in western China. The precipitation in the area north of 25 °N had strong response relationships with the SR and AO. AP affected the precipitation in Northeast China, while WS affected the precipitation in North China and western China. RH affected the precipitation in the regions south of 25 °N. The response relationship between precipitation and CO2 was weakest. The total contribution rate of the influencing factors to the annual precipitation was higher in the west and lower in the east. In the regions west of 100 °E, the total contribution rate of the influencing factors to annual precipitation increased from south to north. In the regions east of 100 °E, the total contribution rate of the influencing factors to the annual precipitation decreased from north and south to the central area, AO and AP contributed little to the annual precipitation across China.

Spatial-Temporal Mode Analysis and Prediction of Outgoing Longwave Radiation over China in 2002–2021 Based on Atmospheric Infrared Sounder Data

Outgoing longwave radiation (OLR) is a key factor to study the radiation balance of the earth–atmosphere system. It is of great significance to explore the temporal and spatial variation characteristics over the OLR value in China region and to predict its future variation trend. We investigate the characteristic distribution of OLR value over China and predict its results in time series using the seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) methods based on the OLR data by the Atmospheric Infrared Sounder (AIRS). The Mann–Kendall (MK) mutation test was used to analyze the annual average of OLR values in China and the mutation points in the four seasons. The empirical orthogonal function (EOF) is used to decompose the spatial characteristics and temporal variation of OLR values in China. The MK mutation test is used to obtain the mutation points in the three seasons of spring, summer and autumn. The cumulative variance contribution of the four modes obtained by EOF decomposition exceeds 70%, and the variance contribution of the first mode exceeds 50%. The prediction accuracy with SARIMA model is 99% and LSTM algorithm is 97%. The results of spatiotemporal analysis show that the OLR value near the equator is significantly higher than that of the north and south poles and decreases with the increase of latitude; the OLR value in spring, summer and autumn is higher than that in winter. The results of the MK test show that there are many mutation points in autumn, and the location of the mutation points cannot be determined. The mutation points in spring and summer meet the confidence interval; the first mode of EOF decomposition has a meridional structure, and the OLR value is dropped within 18 years as a whole. The spatial characteristics of modes 1 and 3 have obvious changes in the Qinghai-Tibet Plateau and Northeast China. The prediction results show that the prediction accuracy of SARIMA is higher than that of LSTM. Therefore, the results predicted by SARIMA may provide a reference for the study of the radiation balance of the earth–atmosphere system in China.

Atmosphere-Ocean Coupled Variability in the Arabian/Persian Gulf

The Arabian Gulf comprises one of the world's most unique and fragile marine ecosystems; it is susceptible to the adverse effects of climate change due to its shallow depth and its location within an arid region that witnesses frequent severe atmospheric events. To reproduce these effects in numerical models, it is important to obtain a better understanding of the region's sea surface temperature (SST) variability patterns, as SST is a major driver of circulation in shallow environments. To this end, here, empirical orthogonal function (EOF) decomposition analysis was conducted to investigate interannual to multi-decadal SST variability in the Gulf from 1982 to 2020, using daily Level 4 Group for High Resolution SST (GHRSST) data. In this way, three dominant EOF modes were identified to contribute the Gulf's SST variability. Significant spatial and temporal correlations were found suggesting that throughout the 39-year study period, SST variability could be attributed to atmospheric changes driven by the El Nio-Southern Oscillation (ENSO), Atlantic Multi-decadal Oscillation (AMO), and Indian Ocean Dipole (IOD) climate modes. Spatial and temporal analyses of the dataset revealed that the average SST was 26.7°C, and that the warming rate from 1982 to 2020 reached up to 0.59°C/decade. A detailed examination of SST changes associated with heat exchange at the air-sea interface was conducted using surface heat fluxes from fifth generation (ERA5) European Centre for Medium-Range Weather Forecasts (ECMWF). Despite the SST warming trend, the accumulation of heat during the study period is suggesting that there was an overall loss of heat (cooling). This cooling reverted into heating in 2003 and has since been increasing.

The Spring Heat Source Over the Qinghai–Tibetan Plateau Linked With the Winter Warm Arctic–Cold Siberia Pattern Impacting Summer Drought in China

The atmospheric heat source over the Qinghai–Tibetan Plateau (QTP) in spring has an important impact on the climate of the surrounding regions. However, there have been few systematic studies of the dominant mode of the heat source and the cross-seasonal connections with the preceding winter and following summer. Using a distinct empirical orthogonal function (DEOF) decomposition method, we obtained the leading mode of the spring heat source over the QTP and the surrounding regions and analyzed its precursors in the previous winter and lagging effects in the following summer. Our results show that the first mode (DEOF1) was characterized by a warm plateau and cold surrounding regions. The positive phase was significantly associated with the warm Arctic–cold Siberia (WACS) pattern (r = 0.39, p = .01) and the La Niña-like SST anomaly in the Pacific in the preceding winter and the following East Asian subtropical summer monsoon (r = –0.44, p = .01), resulting in a widespread drought in China during the following summer. The cold anomaly in Siberia and the warm anomaly at mid-to low latitudes in winter associated with the WACS pattern coincide with the DEOF1 mode of the heat source over the QTP and its surroundings through change of meridional temperature gradient and wave-flow interactions. A mid-latitude wave train excited by the WACS and the thermal difference in the meridional direction of the spring DEOF1 mode caused high-pressure anomalies over the QTP and the mid-latitude region of East Asia, influencing central and eastern China. This anomaly was not conducive to the northward advancement of the East Asian summer monsoon, resulting in drought in most of China in spring and summer. The cross-seasonal relationship between the main mode of the spring heat source on the QTP and the preceding winter WACS pattern and the following East Asian summer monsoon can be used as a reference in climate prediction studies.

Impact of Meteorological Conditions on PM2.5 in Jiangsu Province from 2001 to 2019

The weather research and forecasting model coupled with chemistry (WRF-Chem) was used to investigate the impact of meteorological conditions on PM2.5 in Jiangsu Province from 2001 to 2019. Under the condition of constant emission sources, the strongest positive and negative anomalies of annual PM2.5 concentration caused by meteorological conditions occurred in 2008 and 2001, respectively. Furthermore, their anomalies respectively accounted for 10.5% and -14.3% relative to the long-time averaged annual PM2.5 concentration, indicating that meteorological conditions are an important factor causing the interannual variation in PM2.5 concentration in Jiangsu Province. The empirical orthogonal function decomposition results show that the influence of meteorological conditions on the spatial distribution of PM2.5 concentration in Jiangsu Province is consistent under this mode. Additionally, the boundary layer height, temperature, relative humidity, wind speed, and precipitation all have significant negative correlations with the PM2.5 concentration in Jiangsu Province. Meanwhile, the linear regression equation constructed by the above meteorological factors can characterize the relationship between PM2.5 concentration and meteorological conditions well. Moreover, the correlation between the fitting value and the simulated value was 0.73, which was statistically significant at a 99% confidence level according to a student's t test.

Spatial–temporal distribution and forecasting model of precipitation using dynamic-statistical information fusion

Abstract In order to explore the evolvement mechanism of hydrometeorological elements, spatial–temporal distribution of precipitation in the Huai river basin is studied by statistical drawing and empirical orthogonal function decomposition. How to make an objective combination for the predictive results of precipitation? Information fusion in data assimilation is introduced to merge the improved National Centers for Environmental Prediction coupled forecast system model version 2 (CFSv2) with the multilinear regression model. Firstly, in terms of time, the annual precipitation is apt to decline at most stations within 30 years, and precipitation mainly concentrates in the flood season. The characteristics of spatial distribution are similar to topographic features. It can also be found that precipitation gradually decreases from south to north. Secondly, from statistical forecasting, the relationship between precipitation and global sea surface temperature (SST) is explored. Prediction equation is established with SST and the average precipitation. Thirdly, from dynamic model forecasting, the CFSv2 original model and the CFSv2 statistical downscaling model are used to analyze the influence of model deviation on fusion prediction. The optimum interpolation assimilation method is applied for realizing the optimal integration of statistical and dynamic model prediction. Finally, the standardized precipitation index (SPI) is calculated by the combined forecasting of annual precipitation to evaluate drought conditions. The results show that SST is an important factor affecting precipitation, which may be applied as a forecasting direction with other factors. The merged precipitation prediction skill by the CFSv2 original model and the statistical model do not have the great promotion, which is still lower than the prediction skill only by the statistical model. However, the merged precipitation prediction skill by the CFSv2 statistical downscaling model and the statistical model is better than the prediction skills by the two models mentioned above, respectively. These indicate that when the prediction difference between the models is large, the merged prediction error cannot be minimized. When the prediction skill levels are equal, there is an improvement in the merged result. So, it is necessary to revise the climate dynamic model by downscaling. What is more, the obtained drought levels match the actual disaster conditions, providing theoretical support of hydrology and meteorology for the prevention of natural disasters.

CNES real-time ionospheric product accuracy analysis based on empirical orthogonal function decomposition

CNES real-time ionospheric product accuracy analysis based on empirical orthogonal function decomposition

Spatio-temporal variation characteristics of extreme precipitation over the Dongting Lake basin

Abstract Using the global Moran's I and other statistical methods, the spatio-temporal variation characteristics of extreme precipitation over the Dongting Lake basin are studied based on the daily precipitation at national meteorological stations from 1960 to 2016. The results show that the spatial distribution of extreme precipitation is very uneven. The areas with the maximum of extreme precipitation are located in the Lishui River basin, the middle reaches of the Zishui River and the lower reaches of the Yuanshui River. There is more extreme precipitation on the windward slope than in the basin and the lake area. There are two typical modes for extreme precipitation after the empirical orthogonal function decomposition, namely the north–south mode and the northwest–southeast mode. The extreme precipitation event is more likely to occur in the northwestern and southeastern areas of the Hunan Province. There is an obvious spatial clustering of extreme precipitation over the Dongting Lake basin. The global Moran's I is positive. In addition, the extreme precipitation at most meteorological stations shows an increasing tendency. Also, the middle reaches of the Yuanshui River basin and the Zishui River basin have the most significant increasing tendency. The abrupt change of extreme precipitation occurred around the late 1980s.

Characteristics of Surface Temperature and Its Relationship with Meteorological Elements in China in the Last 73 Years

Climate, as the natural environment on which human life depends, is intricately linked to human society. This paper focuses on the characteristics of temperature and its relationship with meteorological elements in China in the last 73 years. The data of this research is from NCEP/NCAR Reanalysis Monthly Means. This study adopts the Empirical Orthogonal Function (EOF) and Singular Value Decomposition (SVD) methods to study the surface temperature characteristics within China, and the synergistic variation between surface temperature and precipitable water content, wind field, and relative humidity in China. The results show that 1980s is a turning point for changes in surface temperature, precipitable water content, wind field, and relative humidity in China. Before 1980s, the temperature in China is low, while after this period, the temperature in China is high and China’s exposure to global warming has increased. Temperature is dominated by negative potential-phase oscillations with relative humidity and wind fields. In the north, temperature and precipitable water content have negative potential-phase oscillations, while temperature and precipitable water content have positive potential-phase oscillations in the south. In the central region of Xinjiang, temperature and precipitable water content have weak negative potential-phase oscillations, while temperature and wind field have positive potential-phase oscillations.

Spatio-Temporal Characteristics of Dry-Wet Conditions and Boundaries in Five Provinces of Northwest China from 1960 to 2020

The study of dry-wet climate boundaries in the context of climate warming is of great practical significance for improving the environment of ecologically fragile zones and promoting economic and natural sustainable development. In this study, based on the daily meteorological data of 110 stations, using the wetness index, empirical orthogonal function decomposition, regime shift detection test, Fourier power spectrum, and Kriging interpolation, the researchers analyzed the spatiotemporal characteristics of dry-wet conditions and boundaries in five provinces of Northwest China from 1960 to 2020. The results showed that the overall wetness index increased in the past 61 years, but with significant internal differences, among which the western and central climate tended to be warm and wet, and the eastern tended to be warm and dry. The annual wetness index changed abruptly in 1986 with cycles of 3.61 a, 7.11 a and 8.83 a. The mutations occurred correspondingly in spring, summer, autumn, and winter in 1972, 1976, 1983, and 1988, with periods of 3.88 a and 4.92 a, 2.18 a and 2.81 a, 2.15 a, and 2.10 a, respectively. The dry-wet climate boundary has fluctuated markedly since 1960. The extreme arid and arid regions boundary shifted southward and shrank in size until the extreme arid region disappeared in the 2010s. The arid along with semi-arid regions and semi-arid in addition to semi-humid regions boundaries both have two boundary lines, and show the shift of the northwestern boundary to the southeast and the southeastern boundary to the northwest, with the area of the arid together with semi-arid regions shrinking significantly by 5.64%, simultaneously, the area of the semi-humid region area expanding significantly by 84.11%. The boundary of semi-humid and relatively humid regions, and the boundary of relatively humid and humid regions all shifted to the southeast, moreover, the area of the relatively humid region and humid region shrank significantly by 12.08%. The expansion of semi-humid region and the contraction of other climate regions are characteristics of the dry-wet climate variability in five provinces of Northwest China. The area of the three arid climate zones dwindled by 9.61%, and the area of the three humid zones extended by 39.01%. Obviously, the climate inclined to be warm and humid in general.

Ocean Temperature and Color Frontal Zones in the Gulf of Mexico: Where, When, and Why

AbstractThe spatial and temporal patterns of ocean frontal zones in the Gulf of Mexico (GoM) are studied using ocean color and sea surface temperature (SST) imagery collected by the Moderate Resolution Imaging Spectroradiometer/Aqua (MODIS/A) over an 18‐year (2002–2019) period. A gradient‐based front detection algorithm is applied to each daily image to detect frontal features, from which a comprehensive data set of ocean frontal zones (both climatology and time series) is established. Prominent seasonality is found in both ocean color and SST frontal zones while their seasonal variations differ. Major persistent frontal zones are mainly distributed in coastal waters within the 130‐m isobath, and they are often associated with wind forcing, wind‐driven Ekman transport, upwelling/downwelling, river discharge, ocean currents, and topography. The seasonal and interannual variations in frontal distributions are also revealed in the dominant spatial and temporal modes derived from an empirical orthogonal function (EOF) decomposition analysis. These findings may have significant implications on fisheries and ocean ecology research. For example, both ocean color and SST frontal features in certain months are found to coincide with a well‐known fishing ground in the eastern GoM, and surface macroalgae features are found to align well with the identified ocean frontal features.

Dominant physical-biogeochemical drivers for the seasonal variations in the surface chlorophyll-a and subsurface chlorophyll-a maximum in the Bay of Bengal

The seasonal variations and driving mechanisms of the surface and subsurface chlorophyll-a concentrations in the Bay of Bengal are far from resolved, as only a few local, short-term studies have been performed. Hence, this study investigates a comprehensive basin-wide framework of the seasonal variations in the chlorophyll-a concentration, its dominant external forcing, and the internal dynamics of the Bay of Bengal. Multivariate empirical orthogonal function decomposition and heterogeneous correlation analyses are applied to numerous observational, reanalysis, and satellite datasets, including chlorophyll-a, nutrients, temperature, salinity, turbidity, and wind stress curl datasets collected from various sources, including the Copernicus Marine Environment Monitoring Service, World Ocean Atlas, and ERA-Interim. This study suggests that the chlorophyll-a concentrations at both the surface and the subsurface chlorophyll-a maximum (SCM) are higher during summer and early autumn than during the other seasons, especially along the coastal regions and western part of the Bay of Bengal. During summer and early autumn, riverine nutrient inputs, the intrusion of nutritious water from the Arabian Sea, and coastal upwelling are the three dominant drivers controlling the chlorophyll-a concentrations at both the surface and the SCM. The positive wind stress curl-induced uplift of the thermocline increases the nutrient supply and thus significantly enhances the chlorophyll-a concentration at the SCM along the entire western side of the bay during the second half of the year. During spring, the deep euphotic depth plays a vital role in controlling the concentration and depth of the SCM. The depth of the 26 °C isotherm can be used as a proxy of the depth of the SCM. This study provides an improved understanding of the high chlorophyll-a concentrations and their drivers in five potential zones within the Bay of Bengal, which will help to identify the rich marine ecosystems therein.

Spatiotemporal Characteristics of Rainfall in South China from 1967 to 2018

AbstractBased on daily meteorological observation data in South China (SC) from 1967 to 2018, the spatiotemporal characteristics of the precipitation in SC over the past 52 years were studied. Only 8% of the stations showed a significant increase in annual rainfall, and there was no significant negative trend at any weather stations at a confidence level of 90%. Monthly rainfall showed the most significant decreasing and increasing trends in April and November, respectively. During the entire flooding season from April to September, the monthly rainfall at the weather stations in the coastal areas showed almost no significant change. The annual rainfall gradually decreased toward the inland area with the central and coastal areas of Guangdong Province as the high-value rainfall center. By using the empirical orthogonal function decomposition method, it was found that the two main monthly rainfall modes had strong annual signals. The first modal spatial distribution was basically consistent with the average annual rainfall distribution. Based on the environmental background analysis, it was found that during the flooding season, the main water vapor to SC was transported by the East Asian summer monsoon and the Indian summer monsoon. In late autumn and winter, the prevailing wind from northeastern China could not bring much water vapor to SC and led to little precipitation in these two seasons. The spatial distribution of precipitation in SC during summer was more consistent with the moisture flux divergence distribution of the bottom layer from 925 hPa to 1000 hPa, rather than the layer from 700 hPa to 1000 hPa.

Deciphering organization of GOES-16 green cumulus through the empirical orthogonal function (EOF) lens

Abstract. A subset of continental shallow convective cumulus (Cu) cloud fields has been shown to have distinct spatial properties and to form mostly over forests and vegetated areas, thus referred to as “green Cu” (Dror et al., 2020). 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 (GWs) 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.

New statistical prediction scheme for monthly precipitation variability in the rainy season over northeastern China

AbstractThis study focused on seasonal prediction for monthly precipitation over Northeast China (NEC) in the rainy season. A statistical method combining empirical orthogonal function (EOF) decomposition and multi‐linear regression was developed and tested. For each EOF mode of each month in summer, the relationship between the EOF and SSTs in the previous winter was investigated and indices were constructed to be used as predictors. The predictors were required to be physically connected to the predictand and to perform well in cross validation. Monthly rainfall was reconstructed from the predicted time series and the observed spatial load of the first three EOF modes. The results of the leave‐one‐out cross‐validation for 1982–2010, and of the independent validation for 2011–2018, indicate that this new method provides good predictions of monthly precipitation over NEC. There was good agreement between the observed and predicted monthly precipitation, with correlation coefficients of 0.45, 0.40 and 0.55, and hit rates of 73, 62 and 73% for June, July and August precipitation, respectively, for the period 1982 to 2018.

Analyzing the effects of sea ice melting and atmospheric heat transport on the warming around arctic based on comparable analysis and coupling modes

The Arctic warming has become a key signal with global climate change in recent decades. In this study, sea ice volume and whole layer atmospheric heat flux divergence were used to represent the local factor and external transport, respectively. The random forest algorithm was adopted to study the nonlinear effects and variations in importance between the local factors and external transport on Arctic warming between 1979 and 2018. The multivariate empirical orthogonal function decomposition method was applied to explore the coupling structure among sea ice volume, heat flux divergence, and temperature at different altitudes, locate the main passages of atmospheric heat transport to the Arctic, and explore the physical mechanisms of extreme Arctic climate events in the past two years. The analysis results suggest that sea ice has a significant impact on lower-level warming, and the atmospheric heat transport is essential in regulating temperature changes in the middle and upper troposphere. Additionally, on an interdecadal scale, the average state of Arctic warming was found to be strongly related to the Arctic Ocean Oscillation index, explaining the warming trend in the Atlantic sector. The trend is jointly controlled by the heat transport passages in the Pacific and Atlantic Oceans. The abrupt change in 2017–2018 was caused by the increased heat transport in the Pacific Ocean and weakened heat transport in the Atlantic Ocean, leading to abnormal warming and cooling near the two passages. On a seasonal scale, heat transport increase in the three passages located in 1) the Baffin Bay and the Labrador Sea, 2) the Nordic Peninsula and the Barents Sea, 3) the Bering Sea and the East Siberian Sea likely causes regional differences in Arctic summer warming. The increased heat transport in the Bering Sea and East Siberian Sea passage in winter made the Pacific sector significantly warmer. A positive feedback mechanism was created by the change in the circulation field between the temperature and Pacific heat transport.

Influence of interannual variability in estimating the rate and acceleration of present-day global mean sea level

Recent studies have shown that the global mean sea level (GMSL) is accelerating. For improved process understanding and sea level projections, it is crucial to precisely estimate the GMSL acceleration due to externally-forced global climate change. For that purpose, the internal climate variability-related signal of the GMSL needs to be removed from the GMSL record. In the present study, we estimate how the observed GMSL rate has evolved with time over the altimetry era (1993-present), with the objective of determining how it is influenced by the interannual variability. We find that the GMSL rate computed over 5-year moving windows, displays significant interannual variability around 6–7 years and 12–13 years, preventing from robust acceleration estimation. To remove from the observed GMSL time series, the interannual variability, possibly related to internal climate modes, like ENSO, PDO, IOD, NAO or AMO, we use two methods previously widely applied in the literature: (1) multiple linear regression of the GMSL against some climate indices, and (2) Empirical Orthogonal Function (EOF) decomposition of the gridded sea level data to isolate the interannual signal. Although the interannual signal of the corrected GMSL time series is reduced, a cycle around 6–7 years still remains in the GMSL rate. We discuss possible sources of the remaining 6-7-year cycle, including the limitation of the methods used to remove the interannual variability. • Detection of 6-7-year cycle in the rate of rise of the global mean sea level during the altimetry era • Two different methods used for correcting the global mean sea level rise from internal climate variability • Improved estimate of the altimetry-based global mean sea level rate after accounting of internal climate variability • Global mean sea level acceleration of 0.12 mm/year/year over 1993-2019 after correcting for the interannual variability.

Spatiotemporal variability in annual drought severity, duration, and frequency from 1901 to 2020

Drought is currently one of the most severe natural disasters affecting the world. Therefore, characterizing the temporal and spatial characteristics of droughts is critical to managing drought disasters and coping with global climate change. In this study, we apply the ensemble empirical mode decomposition (EEMD), empirical orthogonal function (EOF) decomposition, and the drought event division method of run theory to analyze the temporal and spatial characteristics of meteorological droughts across the globe from 1901 to 2020 using the self-calibrated Palmer drought severity index (scPDSI). We found that the world was gradually becoming wetter from 1901 to 2020, but the severity and duration of severe and extreme drought events also increased. In contrast to the PDSI, which showed no obvious short-period changes, the annual drought severity (ADS) had short cycle changes of 8 and 17 yr. Based on the EOF decomposition results of drought characteristics, dry and wet trends of local PDSI are inconsistent with increasing or decreasing trends of severe and extreme drought events. This research on the temporal and spatial decomposition of severe and extreme drought events provides a new perspective on drought research and analysis, which is critical for the further characterization of drought and the prevention and management of drought disasters.

Annual Cycle of East Asian Precipitation Simulated by CMIP6 Models

Annual cycle is fundamental in the East Asian monsoon (EAM) systems, profoundly governing the spatiotemporal distribution of the East Asian rainfall. The present study identified the dominant modes of the annual cycle in the East Asian rainfall based on the Fourier harmonic analysis and the Empirical Orthogonal Function (EOF) decomposition. We evaluated the performance of the first two leading modes (i.e., EOF-1 and EOF-2) in historical experiments (1979–2014) of the 21 released climate models of phase six of the Coupled Model Intercomparison Project (CMIP6). Comparing with the observation, although the CMIP6 models yield the essential fidelity, they still show considerable systematic biases in the amplitude and phase of the annual cycle, especially in east and south China. Most models exhibit substantial phase delays in the EOF-2 mode of the annual cycle. Some specific models (BCC-ESM1, CanESM5, and GFDL-CM4) exhibiting better performance could capture the observed annual cycle and the underlying physics in climatology and interannual variability. The limited fidelity of the EOF-2 mode of the EAM annual cycle primarily hinders the monsoon variability simulation and thus the reliable future projection. Therefore, the dominant modes of the EAM annual cycle act as the evaluate benchmark in the EAM modelling framework. Their improvement could be one possible bias correction strategy for decreasing the uncertainty in the CMIP6 simulation of the EAM.

Modeling the global ionospheric electron densities based on the EOF decomposition of the ionospheric radio occultation observation

An attempt is made to model the three-dimensional global structures and temporal variations of the ionospheric electron density (Ne) using the radio occultation (RO) Ne profile data obtained by the COSMIC/FORMOSAT-3, CHAMP and GRACE missions during the period from July 2006 to June 2017. The modeling technique we adopted is based on the empirical orthogonal function (EOF) analysis of the RO Ne dataset which is binned with grids of 2.5° in geomagnetic latitudes and 1/3 hr in geomagnetic local time (equivalent to 5° in geomagnetic longitudes) and 10 km in height. The EOF analysis decomposed the binned (gridded) Ne dataset into a series of eigen modes or basis functions (E i ) representing the variations with geomagnetic latitude, geomagnetic local time as well as height and the associated EOF amplitude coefficients (A i ) representing the variations with seasons as well as solar cycle activity. Our results showed that the EOF components (E i , A i ) obtained by the EOF decomposition have different three-dimensional spatial structures with distinct features attributable to different factors or processes controlling the variations of the ionosphere: the first EOF component represents mainly the global mean structure of Ne and its temporal variation with seasons and solar cycle activity; the second EOF component represents mainly the seasonal control of the solar zenith angle on the ionosphere; the third EOF component is representative of the uplifting or lowering of the peak height where Ne reaches its maximum value; the fourth and fifth EOF components represent respectively the variations of the thickness of the ionosphere in the southern and northern hemispheres. It is found that the obtained eigen series converges quickly, with the first five EOF components contributing as high as 98% of the variances of the Ne dataset. We then modeled the EOF coefficients A i obtained by the EOF decomposition using the harmonic functions representing the annual and semi-annual variations, with the solar cycle dependences being taken into account by including the changes of the harmonic amplitudes with the solar irradiance flux index F10.7. The Ne model is constructed using the A i thus modeled and E i obtained by the EOF decomposition. Comparison between the Ne model output results and the COSMIC-2 observational data showed that the modeled results capture well the global structures of the observational data and the model output results have very high linear correlation coefficients with the observational ones (R > 0.9), justifying the modeling technique used in our present study.