Lagged Correlation Analysis Research Articles

Leveraging NASA Soil Moisture Active Passive for Assessing Fire Susceptibility and Potential Impacts Over Australia and California

Wildfires are a major concern around the globe because of the immediate impact they have on people's lives, local ecosystems, and the environment. Soil moisture is one of the most important factors that influence wildfire occurrences and spread. However, it is also one of the most challenging hydrological variables to measure routinely and accurately. Therefore, soil moisture is significantly underutilized in operational wildfire risk applications. Thus, the aim here is to use a well-established operational soil moisture product to isolate the soil moisture-fire relationship and assess the utility of using soil moisture as a leading indicator of potential fire risk. We evaluated the value of remotely-sensed soil moisture observations from the soil moisture active passive sensor for monitoring and predicting fire risk in Australia and California. We quantified the relationship between observed fire activity and soil moisture conditions and analyzed the soil moisture conditions for two extreme fire events. Our findings show that fire activity is strongly associated with soil moisture anomalies. Lagged correlation analysis demonstrated that a remote-sensing based soil moisture product could predict fire activity with a 1–2 month lead-time. Soil moisture anomalies consistently decreased in the months preceding fire occurrence, often from normal to drier conditions, according to a spatiotemporal analysis of soil moisture in two extreme fire events. Overall, our findings indicate that soil moisture conditions prior to large wildfires can aid in their prediction and operational satellite-based soil moisture products such as the one used here have real value for supporting wildfire susceptibility and impacts.

Variations of the Kuroshio in the Luzon Strait Revealed by EOF Analysis of Repeated XBT Data and Sea‐Level Anomalies

AbstractThe Kuroshio in the Luzon Strait (KLS) has long been a focus of research for its complex ocean dynamics; however, it is poorly understood owing to a lack of long‐term in situ observations. Empirical orthogonal function (EOF) analyses were applied to quarterly expendable bathythermograph data (XBT, with a spatial resolution of 10–20 km in the Kuroshio region and 33 km in the other regions) and sea‐level anomaly (SLA) data, to examine the variations of the KLS. The first EOF modes of water temperature and SLA represent seasonal variations. The volume transport of the KLS referring to a 700 m depth reaches its maximum in warm months (19.4 Sv, July–September) and its minimum in cold months (17 Sv, January–March). This variation is similar to that east of Taiwan but is opposite to that east of Luzon, although the Luzon Strait is only 2° distant from the area east of Luzon. The outflow (1.3 Sv) from the South China Sea (SCS) to the Kuroshio in the warm months and the Kuroshio intrusion into the SCS in the cold months are responsible for this seasonal contrast. The second EOF modes of water temperature and SLA are related to the latitudinal movement of the Kuroshio recirculation on a time scale of 2–24 months. Lag correlation and wavelet analysis demonstrate that this mode is triggered by the westward‐propagating SLA along 21°–24°N and is affected by the downstream propagation of low‐frequency variation (mostly annual to biennial) from the North Equatorial Current bifurcation area.

Transport and heat loss of the Pacific Summer Water in the Arctic Chukchi Sea northern slope: Mooring data analysis

We investigated the transport and heat loss of the Pacific Summer Water (PSW) along the Chukchi Sea northern slope using the temperature, salinity, and velocity data of the mooring time series obtained in 2003–2005 (Period 1) and 2015–2019 (Period 2). The PSW at Stations HSN/NHC on the Chukchi Sea northern slope was warmer and fresher in Period 2 than in Period 1. Results from a lag correlation analysis of the temperature time series provide quantitative evidence to suggest that the PSW was transported from Barrow Canyon to Stations HSN/NHC by the northwestward Chukchi Slope Current. The advection time was 7–61days in the layer of 30–60 m. The temperature and salinity mostly decreased during PSW transport. Vertical mixing with the surface cold water could be a potential factor for temperature decrease. This decrease in Period 2 were higher than that in Period 1. This large decrease was probably influenced by stratification rather than wind conditions.

Continental-scale prediction of live fuel moisture content using soil moisture information

Live fuel moisture content (LFMC) is a key factor that determines the flammability of vegetation in ecosystems. Soil moisture (SM) is one of the variables that is known to influence plant water use. The present study analyses the LFMC-SM relationship over Australia using gridded, remote sensing-based LFMC and land surface model-based SM products. A lag-correlation analysis conducted over 60 selected sites shows that the strength of the relationship between LFMC and SM varies from site to site and, in general, is moderately strong (median lag-correlation of ~0.5). However, the strength of the relationship changes with vegetation type and also with soil profile depth. At all the sites, SM is found to be a leading indicator of LFMC. The lag also varies with the location and is found to range from days to months. Based on the location-based correlation analysis, we identify the 0-35 cm SM profile (SM0-35cm) to be the best predictor of LFMC. We developed a simple model to predict daily LFMC, where it is hypothesised that daily variations in LFMC from its annual cycle can be predicted using daily deviations from the annual cycle in SM0-35cm. The annual cycles of LFMC and SM0-35cm are modelled using Fourier cosine series. The averaged (over 60 sites) correlation obtained for the validation period is 0.74 when a time-lag of 14 days is assumed at all locations. When the model is applied nationally at a 5 km grid, the normalised root mean squared error for the validation period is found to be less than 25% in general. The results from the present study highlight a modelling strategy that can be used to address a critical gap in the forecast of spatially and temporally continuous LFMC at regional scales in advance for operational fire management applications.

Ecosystem-Dependent Responses of Vegetation Coverage on the Tibetan Plateau to Climate Factors and Their Lag Periods

The spatiotemporal variation characteristics of the Normalized Difference Vegetation Index (NDVI) and its climate response patterns are of significance in deepening our understanding of regional vegetation and climate change. The response of vegetation to climate factors varies spatially and may have lag periods. In this paper, we studied the spatiotemporal responses of vegetation to climatic factors on an ecosystem-dependent scale using GIMMS NDVI3g data and climatic parameters. Pure pixels with a single vegetation type were firstly extracted to reduce the influence of mixed vegetation types. Then, a lag correlation analysis was used to explore the lag effects of climatic parameters affecting NDVI. Finally, the stepwise regression method was adopted to calculate the regression equation for NDVI and meteorological data with the consideration of effect lag times. The results show that precipitation has significant lag effects on vegetation. Temperature is the main climatic factor that affects most vegetation types at the start of growing season. At the end of growing season, the temperate desert, temperate steppe, and temperate desert steppe are greatly affected by precipitation. Moreover, the alpine steppe, alpine desert, alpine meadow, and alpine sparse vegetation are greatly affected by temperature. The needleleaf forest, subalpine scrub, and broadleaf evergreen forest are sensitive to sunshine percentage during almost the whole growing season. These findings could contribute to a better understanding of the drivers and mechanisms of vegetation degradation on the Tibetan Plateau.

Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

Epidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number (R_t). The relationship between public health interventions and R_t was explored, firstly using a hierarchical clustering algorithm on initial R_t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, R_t, and daily incidence counts in subsequent months. The impact of updating R_t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future R_t (75 days lag), while a lower R_t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated R_t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when R_t was kept constant. Monitoring the evolution of R_t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated R_t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of R_t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

Salinity response to atmospheric forcing of the Terra Nova Bay polynya, Antarctica

AbstractThe density and salinity of High Salinity Shelf Water, a key component of Antarctic Bottom Water emanating from the Ross Sea, are intensified by brine rejection induced by ice formation within the Terra Nova Bay (TNB) polynya. Ocean mooring data from 2007, meteorological observations from automatic weather stations and a satellite-derived history of the opening of TNB polynya delineate variability in water column salinity linked to atmospheric forcing, with a period on the order of 10 days. Lagged correlation analysis indicates that on average salinity response lags the polynya opening by 2 days and the wind forcing by 5 days. We find stronger correlations of salinity with the wind during March through May and with the polynya open-water fraction during June through October, with decreasing lags in the latter period. A one-dimensional mixed-layer model incorporating thermodynamic ice formation captures the oscillations in salinity. A process study shows that the variability in the polynya open-water fraction governs the final salinity attained by the model as well as the salinity cycling. Variability in surface heat fluxes modulates that effect. Our work suggests that there is a more complex relationship between salinity, the polynya open-water fraction, and atmospheric forcing than previously suggested.

Precipitation changes and their relationships with vegetation responses during 1982–2015 in Kunhar River basin, Pakistan

AbstractPrecipitation is a major determinant of vegetation growth. The impact of precipitation variability is more pronounced in ecosystems where sensitive vegetation is apparent. Therefore, understanding the relationship between precipitation and vegetation is vital to guide appropriate measures towards fragile biomes. We investigated the trends and correlations between precipitation and normalized difference vegetation index (NDVI) for 1982–2015 over the Kunhar River basin, Pakistan, using satellite-derived NDVI and globally available interpolated precipitation datasets. Subsequently, we attempt to identify vegetation types that are influenced by precipitation changes. Results show a general decreasing trend in vegetation activity as we go from southern to northern portions of the basin. This decrease is also accompanied by the similarly decreasing precipitation trend in the same direction. The similarity of spatial patterns between the two variables can indicate that, in general, precipitation is playing a guiding role in determining vegetation distribution in the basin. Our lagged correlation analysis revealed that strong precipitation–vegetation correlations (r > 0.75) are rare in the basin. Agricultural and forested areas show moderate correlations (0.5 < r < 0.75) when NDVI is correlated with the previous month's precipitation values (lag1). In simultaneous month correlation (lag0) and the rest of the lagged correlations (lag2 and lag3), a weak association between precipitation and NDVI is observed. The moderate and weak correlations over the basin might indicate that precipitation is not the only factor influencing vegetation growth in the Kunhar River basin. Other climatic and biogeographic factors such as temperature, solar radiation, topography and soil characteristics also play additional roles in vegetation activities. The results can provide a technical basis and valuable reference to ecological management strategies in the Kunhar River basin for concerned decision-makers and stakeholders.

Assimilation of Satellite-Retrieved Sea Ice Concentration and Prospects for September Predictions of Arctic Sea Ice

AbstractThe current GFDL seasonal prediction system achieved retrospective sea ice extent (SIE) skill without direct sea ice data assimilation. Here we develop sea ice data assimilation, shown to be a key source of skill for seasonal sea ice predictions, in GFDL’s next-generation prediction system, the Seamless System for Prediction and Earth System Research (SPEAR). Satellite sea ice concentration (SIC) observations are assimilated into the GFDL Sea Ice Simulator version 2 (SIS2) using the ensemble adjustment Kalman filter (EAKF). Sea ice physics is perturbed to form an ensemble of ice–ocean members with atmospheric forcing from the JRA-55 reanalysis. Assimilation is performed every 5 days from 1982 to 2017 and the evaluation is conducted at pan-Arctic and regional scales over the same period. To mitigate an assimilation overshoot problem and improve the analysis, sea surface temperatures (SSTs) are restored to the daily Optimum Interpolation Sea Surface Temperature version 2 (OISSTv2). The combination of SIC assimilation and SST restoring reduces analysis errors to the observational error level (~10%) from up to 3 times larger than this (~30%) in the free-running model. Sensitivity experiments show that the choice of assimilation localization half-width (190 km) is near optimal and that SIC analysis errors can be further reduced slightly either by reducing the observational error or by increasing the assimilation frequency from every 5 days to daily. A lagged-correlation analysis suggests substantial prediction skill improvements from SIC initialization at lead times of less than 2 months.

Time-variant error characterization of SMAP and ASCAT soil moisture using Triple Collocation Analysis

Knowledge about spatiotemporal error characteristics of remotely sensed soil moisture (SM) products is essential for correctly interpreting observational information and optimally assimilating them into hydrological models. This work aims to (i) investigate the relative difference between time-invariant and time-variant daily SM errors of Advanced Scatterometer (ASCAT) and Soil Moisture Active Passive (SMAP) products and (ii) analyze correlation of the daily SM errors with surface biomass quantified by the Leaf Area Index (LAI) and rainfall. The time-invariant error denotes an aggregate error magnitude during the whole investigation period and the time-variant error at daily time scale also refers to an aggregate error magnitude but for the period of the 100-day time window centered at the day to be processed. The time-invariant and daily SM errors are estimated using the Triple Collocation Analysis (TCA) applied to ASCAT and SMAP SM retrievals along with the Global Land Data Assimilation System version 2.1 (GLDAS2) SM product for the period of April 2015 – January 2020. Results indicate the relative difference between time-invariant and time-variant daily errors is notable for both ASCAT and SMAP SM products. The relative difference in units percentage denotes the ratio of difference value between time-invariant and daily errors to time-invariant error itself. The daily TCA error fluctuated at 43% value from time-invariant error for ASCAT SM and at 47% value for SMAP SM on a global average. When averaged globally, temporal mean of time-variant daily errors was relatively smaller than time-invariant error by -27% for ASCAT SM and -18% for SMAP SM, respectively. In tropical areas, the relative difference between time-invariant and daily errors is large during the dry season and becomes small when rainy season comes. When the peak lagged correlation is used, daily errors exhibit a stronger correlation with rainfall than they do with LAI in 61% of landmass pixels for ASCAT SM and 66% of landmass pixels for SMAP SM. LAI cannot be used to predict temporal variability of time-variant SM errors in barren land. Lagged correlation analysis reveals rainfall peak coincides with SM error peak in areas featured with low vegetation cover, including barren land, grasslands, and open shrublands. By contrast, the LAI peak comes after the SM error peak in all cases. Savannas and woody savannas are a special case as SM error peak comes first, followed by rainfall then LAI peaks. In summary, ASCAT and SMAP time-variant error varies with a large deviation from time-invariant errors and its temporal variation shows a stronger association with rainfall than changing LAI.

Three decades of depth-dependent groundwater response to climate variability and human regime in the transboundary Indus-Ganges-Brahmaputra-Meghna mega river basin aquifers

Groundwater plays a major role in human adaptation and ecological sustainability against climate variability by providing global water and food security. In the Indus-Ganges-Brahmaputra-Meghna aquifers (IGBM), groundwater abstraction has been reported to be one of the primary contributors to groundwater storage variability. However, there is still a lack of understanding on the relative influence of climate and abstraction on groundwater. Data-guided statistical studies are reported to be crucial in understanding the human-natural complex system. Here, we attributed the long-term (1985–2015) impact of local-precipitation, global-climate cycles, and human influence on multi-depth groundwater levels (n=6753) in the IGBM using lag correlation analysis, wavelet coherence analysis, and regression-based dominance analysis. Our findings highlight the variable patterns of phase lags observed between multi-depth groundwater levels and precipitation depending on the different nature of climatic and anthropogenic drivers in different parts of the basin. We observed intuitive responses, i.e., rapid response in shallow groundwater and relatively delayed responses to the global climate patterns with increasing depth. However, in the most exploited areas, the hydrological processes governing the groundwater recharge are overwhelmed by unsustainable groundwater abstraction, thus decoupling the hydro-climatic continuum. Our results also suggest groundwater abstraction to be the dominant influence in most of the basin, particularly at the greater depth of the aquifer, thus highlighting the importance of understanding multi-depth groundwater dynamics for future groundwater management and policy interventions.

Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco

In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000–2017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting.

The Effect of Tropical Cyclone Nicholas (11–20 February 2008) on Sea Level Anomalies in Indonesian Waters

As reported extensively in both electronic and print media in Indonesia, high wave and anomalously high sea level phenomena occurred in February 2008 in Indonesian waters, mainly along the western coast of Sumatra and the southern coasts of Java-Bali. Tropical Cyclone (TC) Nicholas, occurring in northwestern coastal waters of Australia between 11 and 20 February 2008, might have contributed to the existence of these phenomena in the Indonesian region. This study focused on investigating the effect of TC Nicholas on the increases in sea levels in the Indonesian waters by analyzing residual water levels (non-astronomic tide). In this regard, a storm tide event (the sum of the astronomical tide and storm surge generated by the TC Nicholas) was simulated in this region using the Regional Ocean Modeling System (ROMS). The residual water levels were obtained by removing the tidal part (astronomic tide) from the ROMS simulated total water levels. In addition, to confirm possible influences of TC Nicholas, a lagged correlation analysis was applied between atmospheric pressure at the center of TC Nicholas and residual water level oscillations in the Indonesian waters. It was found that the residual water levels showed a strong correlation with the atmospheric pressure at the center of TC Nicholas in some areas of the Indonesian seas, such as the western coast of Sumatra, the southern coast of Java, Lesser Sunda Islands, and the southern coast of Papua. The increased sea levels on the western coast of Sumatra are up to 16 cm, with TC Nicholas leading the residual water level by 4.18 days (TL: time lag). Meanwhile, they are up to 20 cm (TL = 5.75 days), 21 cm (TL = 1.12 days), and 38 cm (TL = 3.96 days) on the southern coast of Java, the Lesser Sunda Islands, and the southern coast of Papua, respectively. The results of this study could be used as an initial assessment to investigate the most vulnerable Indonesian coastal areas to the impact of the TC and they might be significantly beneficial for designing both a proper disaster risk reduction program and investment policies in the region, particularly in the context of flood risk reduction and adaptation.

Assessment of drought frequency, severity, and duration and its impacts on vegetation greenness and agriculture production in Mediterranean dryland: A case study in Tunisia

Drought is among the highest-impact natural hazards affecting drylands around the world in a warming climate. The Mediterranean region, including Tunisia, is projected to experience the most predominant drying trends worldwide. However, a detailed regional scale study of drought for Tunisia has been limited, hampering an assessment of drought impact on the ecosystem and society. This study explored drought characteristics and its effect on vegetation greenness and agricultural productivity in three vegetation zones of Tunisia during 1982–2011, taking advantage of both meteorological and soil moisture drought indices and identifying the most appropriate index for each zone. The results revealed that meteorological droughts were short and frequent, triggering soil moisture droughts that were long-lasting and intense. The standardized precipitation index was identified as the best indicator of vegetation and agricultural droughts in the Northern forest, while the Palmer Drought Severity Index was best in the Central steppe and Southern desert (no crop data in the Southern desert). The lag-correlation analysis revealed that the response of both vegetation greenness and wheat productivity to droughts was more pronounced and had a longer significant lag in the Central steppe than in the other regions. These results suggest that arable land species (Central steppe and Southern desert), characterized by shallow roots, have a rapid response to rainfall variability when compared with forest species (Northern forest), which have deep roots allowing them an adequate supply of moisture. The region-specific indices identified here will provide a useful measure for drought monitoring and mitigation in Tunisia.

Assessing the Impact of ENSO on Agriculture Over Africa Using Earth Observation Data

The El Nino–Southern Oscillation (ENSO) is one of the strongest drivers of climate variability, which directly influences agricultural production. The present study aims at assessing the impact of ENSO on agriculture in southern and eastern Africa by (1) exploring the association between ENSO, vegetation condition and soil moisture, and (2) analyzing the difference in soil moisture and vegetation condition for two extreme ENSO phases (El Nino and La Nina). Our results showed that vegetation conditions are significantly correlated with ENSO and exhibit a clear dipole pattern that reverses between El Nino and La Nina. Lagged correlation analysis confirmed the ability of soil moisture and ENSO to predict vegetation conditions. In general, the temporal and spatial evolution of soil moisture and vegetation responses showed the expected dipole pattern during the El Nino and following La Nina events. Results showed that ENSO impact on crop yield varied with geographic location, crop types, and ENSO phases. For example, yields in La Nina years have been higher in southern Africa but lower in eastern Africa. Maize yield decreases associated with El Nino events were usually larger than corresponding yield increases during La Nina events over southern Africa. Our findings highlight the impact of ENSO on agricultural production, which has significant potential to enhance the agriculture and food security-related early warning system.

ENSO signatures observed in tropical tropopause layer parameters using long-term COSMIC RO data

The El Nino Southern Oscillation (ENSO) signatures interpreted in different tropical tropopause layer (TTL) parameters are investigated by using long-term Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)-1 radio occultation temperature data from July 2006 to March 2019. The TTL parameters include the cold point tropopause height (CPT-H) and corresponding temperature (CPT-T), lapse rate tropopause height (LRT-H) and corresponding temperature (LRT-T), convective outflow level height (COH), and TTL thickness. Results indicate that the warm phase of ENSO is associated with prominent negative CPT-T anomalies, whereas the cold phase of ENSO is associated with positive CPT-T anomalies over the tropical central and eastern Pacific Ocean. Further, correlation analysis is carried out between TTL parameters obtained over the Nino 3.4 region (5°N–5°S, 170°W–120°W) and sea surface temperature Nino 3.4 Index. We found weak positive correlations in CPT-H (0.37) and LRT-H (0.49), and moderate negative correlations in CPT-T (− 0.62), LRT-T (− 0.64), and TTL thickness (− 0.5) with Nino 3.4 Index. Interestingly, a higher correlation of 0.8 is noticed between COH and the Nino 3.4 Index. Furthermore, lag correlation analysis reveals that a 4-month lag is noticed in tropopause heights (CPT-H and LRT-H) and a 2-month lag is evident in tropopause temperatures (CPT-T and LRT-T) with the Nino 3.4 Index. In addition, only 1-month lag is noticed between COH and Nino 3.4 index. With a high correlation and 1-month lag with the Nino 3.4 index, it is concluded that the COH is the most suitable TTL parameter to detect the ENSO signatures among the TTL parameters.

Remote Forcing of Tasman Sea Marine Heatwaves

AbstractRecent marine heatwave (MHW) events in the Tasman Sea have had dramatic impacts on the ecosystems, fisheries, and aquaculture off Tasmania’s east coast. However, our understanding of the large-scale drivers (forcing) and potential predictability of MHW events in this region off southeast Australia is still in its infancy. Here, we investigate the role of oceanic Rossby waves forced in the interior South Pacific on observed MHW occurrences off southeast Australia from 1994 to 2016, including the extreme 2015/16 MHW event. First, we used an upper-ocean heat budget analysis to show that 51% of these historical Tasman Sea MHWs were primarily due to increased East Australian Current (EAC) Extension poleward transports through the region. Second, we used lagged correlation analysis to empirically connect the EAC Extension intensification to incoming westward-propagating sea surface height (SSH) anomalies from the interior South Pacific. Third, we dynamically analyzed these SSH anomalies using simple process-based baroclinic and barotropic Rossby wave models forced by wind stress curl changes across the South Pacific. Finally, we show that associated monthly SSH changes around New Zealand may be a useful index of western Tasman Sea MHW predictability, with a lead time of 2–3 years. In conclusion, our findings demonstrate that there is potential predictability of advection-dominated MHW event likelihoods in the EAC Extension region up to several years in advance, due to the deterministic contribution from baroclinic and barotropic Rossby waves in modulating the EAC Extension transports.

The dominant invading paths of extreme cold surges and the invasion probabilities in China

AbstractBased on daily temperature observation dataset with high quality and spatial resolution, this paper revealed the dominant invading paths of the extreme cold surges (ECSs) within China by using the fifth percentile of temperature drops instead of the traditional unified threshold in the operation. The maximal probabilities of the ECSs from the upstream regions to the down‐stream regions along the invading paths are also provided. Results indicate the distribution of the fifth percentile is quite different in various regions, with the highest value centers of more than 8°C in northern China, and a secondary center of 6–7°C in southern China. While in the areas between the Yellow River basin and the Yangtze River basin, the two longest rivers in China, the values are below 5°C. This result explains the unreasonable frequency gap between the two rivers in previous unified threshold. Based on the distribution, the whole country is roughly divided into 18 regions at an approximate box of 5°N × 10°E to acquire clear and objective invading paths by the lead–lag correlation analysis and an objective identification of the paths. Results show the ECS invading path in northern China has a dominant west or northwest track. But in southern China, the invading paths are more complex, generally from north to south. In China, about 30–40% ECS cases in the upstream regions can lead to the ECSs in the down‐stream regions along the dominant paths. By the identification and the lead–lag correlation, northwestern China could be regarded as the source of ECSs invading other regions.

Multiscale teleconnection analysis of monthly total and extreme precipitations in the Yangtze River Basin using ensemble empirical mode decomposition

AbstractThe multiscale analysis of precipitation teleconnections with climate indices is of great significance to better understand the regional response of the precipitation variability under the different time scales to global climate change. In this study, the inherent cycles of monthly total and extreme precipitations during 1965–2016 in the Yangtze River basin (YRB) and the climate indices influencing their periodic oscillations and long‐term trends are investigated by the complete ensemble empirical mode decomposition, lag correlation analysis and stepwise variable selection. Results show that the total and extreme precipitations have experienced increasing long‐term trends in the western region of the upper reach of YRB (U‐YRB) and most parts of the middle and lower reaches of YRB (ML‐YRB), and decreasing trends in the middle region of U‐YRB. The identified climate indices significantly affecting total and extreme precipitations are almost identical in the U‐YRB and ML‐YRB. The sea surface temperature anomalies over East China Sea (ECS), South China Sea (SCS), Kuroshio (KC) and Bay of Bengal (BB) with specific time lags, solar flux with 12‐month lag, and the simultaneous global average temperature anomalies (GT) and trans‐Niño index have strong linkages with precipitation components under the particular time scales in the YRB. Specifically, the ECS, SCS, KC and BB with specific time lags are identified as effective indicators of periodic oscillations of total and extreme precipitations, while the simultaneous GT is powerful for capturing their long‐term trends. The identified climate indices are further demonstrated to provide significant predictive information for total and extreme precipitations and their periodic oscillations. Moreover, their forecast ability is higher in the U‐YRB than in the ML‐YRB, which detects a strong correlation with the randomness of precipitation. Additionally, monthly total precipitation and its periodic oscillations can be predicted better than extreme precipitation and its periodic oscillations by the identified climate indices.

Climate and Land-Use Controls on Surface Water Diversions in the Central Valley, California

California’s Central Valley (CV) is one of the most productive agricultural regions in the world, enabled by the conjunctive use of surface water and groundwater. We investigated variations in the CV’s managed surface water diversions relative to climate variability. Using a historical record (1979−2010) of diversions from 531 sites, we found diversions are largest in the wetter Sacramento basin to the north, but most variable in the drier Tulare basin to the south. A rotated empirical orthogonal function (REOF) analysis finds 72% of the variance of diversions is captured by the first three REOFs. The leading REOF (35% of variance) exhibited strong positive loadings in the Tulare basin, and the corresponding principal component time-series (RPC1) was strongly correlated (ρ > 0.9) with contemporaneous hydrologic variability. This pattern indicates larger than average diversions in the south, with neutral or slightly less than average diversions to the north during wet years, with the opposite true for dry years. The second and third REOFs (20% and 17% of variance, respectively), were strongest in the Sacramento basin and San Francisco Bay−Delta. RPC2 and RPC3 were associated with variations in agricultural- and municipal-bound diversions, respectively. RPC2 and RPC3 were also moderately correlated with 7-year cumulative precipitation based on lagged correlation analysis, indicating that diversions in the north and central portions of the CV respond to longer-term hydrologic variations. The results illustrate a dichotomy of regimes wherein diversions in the more arid Tulare are governed by year-to-year hydrologic variability, while those in wetter northern basins reflect land-use patterns and low-frequency hydrologic variations.