Analysis and prediction of marine heatwaves in the Western North Pacific and Chinese coastal region

Over the past decade, marine heatwaves (MHWs) research has been conducted in almost all of the world’s oceans, and their catastrophic effects on the marine environment have gradually been recognized. Using the second version of the Optimal Interpolated Sea Surface Temperature analysis data (OISSTV2) from 1982 to 2014, this study analyzes six MHWs characteristics in the Western North Pacific and Chinese Coastal region (WNPCC, 100°E ∼ 180°E, 0° ∼ 65°N). MHWs occur in most WNPCC areas, with an average frequency, duration, days, cumulative intensity, maximum intensity, and mean intensity of 1.95 ± 0.21 times/year, 11.38 ± 1.97 days, 22.06 ± 3.84 days, 18.06 ± 7.67 °Cdays, 1.84 ± 0.50°C, and 1.49 ± 0.42 °C, respectively, in the historical period (1982 ~ 2014). Comparing the historical simulation results of 19 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) with the OISSTV2 observations, five best-performing models (GFDL-CM4, GFDL-ESM4, AWI-CM-1-1-MR, EC-Earth3-Veg, and EC-Earth3) are selected for MHWs projection (2015 ~ 2100). The MHWs characteristics projections from these five models are analyzed in detail under the Shared Socio-economic Pathway (SSP) 1-2.6, 2-4.5 and 5-8.5 scenarios. The projected MHWs characteristics under SSP5-8.5 are more considerable than those under SSP1-2.6 and 2-4.5, except for the MHWs frequency. The MHWs cumulative intensity is 96.36 ± 56.30, 175.44 ± 92.62, and 385.22 ± 168.00 °Cdays under SSP1-2.6, 2-4.5 and 5-8.5 scenarios, respectively. This suggests that different emission scenarios have a crucial impact on MHW variations. Each MHWs characteristic has an obvious increasing trend except for the annual occurrences. The increase rate of MHWs cumulative intensity for these three scenarios is 1.02 ± 0.83, 3.83 ± 1.43, and 6.70 ± 2.61 °Cdays/year, respectively. The MHWs occurrence area in summer is slightly smaller than in winter, but the MHWs average intensity is stronger in summer than in winter

A Two-Dimensional Variational Scheme for Merging Multiple Satellite Altimetry Data and Eddy Analysis

With the increasing number of satellite altimeters in orbit, the effective resolution of merged multiple satellite altimetry data can be improved. We implement a two-dimensional variational (2-DVar) method to merge multiple satellite altimetry data and produce a daily gridded absolute dynamic topography (ADT) dataset with a grid size of 0.08 degrees. We conduct an observing system simulation experiment (OSSE), and the results show that the merged ADT dataset has an effective resolution of about 210 km. Compared with an independent sea surface temperature (SST) data, fine-scale structures can also be observed in the geostrophic flow of the new dataset. A relationship between effective resolution and the radius of a detected eddy is established and used for eddy analysis in the East China Sea (ECS) region. We observe that eddies in the open ocean are more numerous, have larger radii and live longer than those in other areas

Meteorological Drought Variability over Africa from Multisource Datasets

This study analyses the spatiotemporal variability of meteorological drought over Africa and its nine climate subregions from an ensemble of 19 multisource datasets (gauge-based, satellite-based and reanalysis) over the period 1983–2014. The standardized precipitation index (SPI) is used to represent drought on a 3-month scale. We analyse various drought characteristics (duration, events, frequency, intensity, and severity) for all drought months, and moderate, severe, and extreme drought conditions. The results show that drought occurs across the continent, with the equatorial regions displaying more negative SPI values, especially for moderate and severe droughts. On the other hand, Eastern Sahara and Western Southern Africa portray less negative SPI values. The study also reveals that extreme drought months have the largest interannual variability, followed by all drought months and severe drought months. The trend analysis of SPI shows a significantly increasing trend in drought episodes over most regions of Africa, especially tropical areas. Drought characteristics vary greatly across different regions of Africa, with some areas experiencing longer and more severe droughts than others. The equatorial region has the highest number of drought events, with longer durations for severe and extreme drought months. The Eastern Sahara region has a low number of drought events but with longer durations for moderate, severe, and extreme drought months, leading to an overall higher drought severity over the area. In contrast, Western Southern Africa and Madagascar display a consistently low drought severity for all categories. The study demonstrates the importance of conducting drought analysis for different drought levels instead of using all drought months. Drought management and adaptation strategies need to enhance community resilience to changing drought situations and consider drought variability in order to mitigate different impacts of drought across the continent

Spatio-Temporal Analysis of Drought Variability in Myanmar Based on the Standardized Precipitation Evapotranspiration Index (SPEI) and Its Impact on Crop Production

Drought research is an important aspect of drought disaster mitigation and adaptation. For this purpose, we used the Standardized Precipitation Evapotranspiration Index (SPEI) to investigate the spatial-temporal pattern of drought and its impact on crop production. Using monthly precipitation (Precip) and temperature (Temp) data from 1986–2015 for 39 weather stations, the drought index was obtained for the time scale of 3, 6, and 12 months. The Mann–Kendall test was used to determine trends and rates of change. Precip and Temp anomalies were investigated using the regression analysis and compared with the drought index. The link between drought with large-scale atmospheric circulation anomalies using the Pearson correlation coefficient (R) was explored. Results showed a non-uniform spatial pattern of dryness and wetness which varied across Myanmar agro-ecological zones and under different time scales. Generally, results showed an increasing trend for the SPEI in the three-time scales, signifying a high tendency of decreased drought from 1986–2015. The fluctuations in dryness/wetness might linked to reduction crop production between 1986–1999 and 2005, 2008, 2010, 2013 cropping years. Results show relationship between main crops production and climate (teleconnection) factors. However, the low correlation values (i.e., <0.49) indicate the extent of the relationship within the natural variability. However, readers are urged to interpret this result cautiously as reductions in crop production may also be affected by other factors. We have demonstrated droughts evolution and trends using weather stations, thus providing useful information to aid policymakers in developing spatially relevant climate change adaptation and mitigation management plans for Myanmar

DataSheet_1_Analysis and prediction of marine heatwaves in the Western North Pacific and Chinese coastal region.pdf

Over the past decade, marine heatwaves (MHWs) research has been conducted in almost all of the world’s oceans, and their catastrophic effects on the marine environment have gradually been recognized. Using the second version of the Optimal Interpolated Sea Surface Temperature analysis data (OISSTV2) from 1982 to 2014, this study analyzes six MHWs characteristics in the Western North Pacific and Chinese Coastal region (WNPCC, 100°E ∼ 180°E, 0° ∼ 65°N). MHWs occur in most WNPCC areas, with an average frequency, duration, days, cumulative intensity, maximum intensity, and mean intensity of 1.95 ± 0.21 times/year, 11.38 ± 1.97 days, 22.06 ± 3.84 days, 18.06 ± 7.67 °Cdays, 1.84 ± 0.50°C, and 1.49 ± 0.42 °C, respectively, in the historical period (1982 ~ 2014). Comparing the historical simulation results of 19 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) with the OISSTV2 observations, five best-performing models (GFDL-CM4, GFDL-ESM4, AWI-CM-1-1-MR, EC-Earth3-Veg, and EC-Earth3) are selected for MHWs projection (2015 ~ 2100). The MHWs characteristics projections from these five models are analyzed in detail under the Shared Socio-economic Pathway (SSP) 1-2.6, 2-4.5 and 5-8.5 scenarios. The projected MHWs characteristics under SSP5-8.5 are more considerable than those under SSP1-2.6 and 2-4.5, except for the MHWs frequency. The MHWs cumulative intensity is 96.36 ± 56.30, 175.44 ± 92.62, and 385.22 ± 168.00 °Cdays under SSP1-2.6, 2-4.5 and 5-8.5 scenarios, respectively. This suggests that different emission scenarios have a crucial impact on MHW variations. Each MHWs characteristic has an obvious increasing trend except for the annual occurrences. The increase rate of MHWs cumulative intensity for these three scenarios is 1.02 ± 0.83, 3.83 ± 1.43, and 6.70 ± 2.61 °Cdays/year, respectively. The MHWs occurrence area in summer is slightly smaller than in winter, but the MHWs average intensity is stronger in summer than in winter.</p

Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product

This study investigated the capability of the Weather Research and Forecasting (WRF) model to simulate seven different heavy precipitation (PRE) events that occurred across East Africa in the summer of 2020. The WRF model outputs were evaluated against high-resolution satellite-based observations, which were obtained from prior evaluations of several satellite observations with 30 stations’ data. The synoptic conditions accompanying the events were also investigated to determine the conditions that are conducive to heavy PRE. The verification of the WRF output was carried out using the area-related root mean square error (RMSE)-based fuzzy method. This method quantifies the similarity of PRE intensity distribution between forecast and observation at different spatial scales. The results showed that the WRF model reproduced the heavy PRE with PRE magnitudes ranging from 6 to >30 mm/day. The spatial pattern from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification-Climate Data Record (PERSIANN-CCS-CDR) was close to that of the WRF output. The area-related RMSE with respect to observation showed that the error in the model tended to reduce as the spatial scale increased for all the events. The WRF and high-resolution satellite data had an obvious advantage when validating the heavy PRE events in 2020. This study demonstrated that WRF may be used for forecasting heavy PRE events over East Africa when high resolutions and subsequent simulation setups are used

Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product

This study investigated the capability of the Weather Research and Forecasting (WRF) model to simulate seven different heavy precipitation (PRE) events that occurred across East Africa in the summer of 2020. The WRF model outputs were evaluated against high-resolution satellite-based observations, which were obtained from prior evaluations of several satellite observations with 30 stations&rsquo; data. The synoptic conditions accompanying the events were also investigated to determine the conditions that are conducive to heavy PRE. The verification of the WRF output was carried out using the area-related root mean square error (RMSE)-based fuzzy method. This method quantifies the similarity of PRE intensity distribution between forecast and observation at different spatial scales. The results showed that the WRF model reproduced the heavy PRE with PRE magnitudes ranging from 6 to &gt;30 mm/day. The spatial pattern from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification-Climate Data Record (PERSIANN-CCS-CDR) was close to that of the WRF output. The area-related RMSE with respect to observation showed that the error in the model tended to reduce as the spatial scale increased for all the events. The WRF and high-resolution satellite data had an obvious advantage when validating the heavy PRE events in 2020. This study demonstrated that WRF may be used for forecasting heavy PRE events over East Africa when high resolutions and subsequent simulation setups are used

Comparative analysis of four types of mesoscale eddies in the North Pacific Subtropical Countercurrent region - part II seasonal variation

The North Pacific Subtropical Countercurrent area (STCC) is high in mesoscale eddy activities. According to the rotation direction of the eddy flow field and the sign of temperature anomaly within the eddy, they can be divided into four categories: cyclonic cold-core eddy (CCE), anticyclonic warm-core eddy (AWE), cyclonic warm-core eddy (CWE) and anticyclonic cold-core eddy (ACE). CCE and AWE are called normal eddies, and CWE and ACE are named abnormal eddies. Based on the OFES data and vector geometry automatic detection method, we find that at the sea surface, the maximum monthly number of the CCE, AWE, CWE, and ACE occurs in December (765.70 ± 52.05), January (688.20 ± 82.53), August (373.40 ± 43.09) and August (533.00 ± 56.92), respectively. The number of normal eddies is more in winter and spring, and less in summer and autumn, while abnormal eddies have the opposite distribution. The maximum rotation velocity of the four types of eddies appears in June (11.71 ± 0.75 cm/s), June (12.24 ± 0.86 cm/s), May (10.63 ± 0.99 cm/s) and June (9.97 ± 0.91 cm/s), which is fast in winter and spring. The moving speed of the four types of eddies is almost similar (about 10 ~ 11 cm/s). The amplitude of normal and abnormal eddies is both high in summer and autumn, and low in winter and spring, with larger amplitudes in normal than abnormal eddies. The eccentricity (defined as the eccentricity of the ellipse obtained by fitting the eddy boundary) of the four types of eddies is also close to each other, and their variation ranges from 0.7 to 0.8, with no apparent seasonal variation. The vertical penetration depth, which has no significant seasonal difference, is 675.13 ± 67.50 m in cyclonic eddies (CCE and CWE), which is deeper than that 622.32 ± 81.85 m in anticyclonic eddies (ACE and AWE). In addition, increasing the defined temperature threshold for abnormal eddies can significantly reduce their numbers but does not change their seasonal variation trend

Spatiotemporal Characteristics and Trend Analysis of Two Evapotranspiration-Based Drought Products and Their Mechanisms in Sub-Saharan Africa

Drought severity still remains a serious concern across Sub-Saharan Africa (SSA) due to its destructive impact on multiple sectors of society. In this study, the interannual variability and trends in the changes of the self-calibrating Palmer Drought Severity Index (scPDSI) based on the Penman–Monteith (scPDSIPM) and Thornthwaite (scPDSITH) methods for measuring potential evapotranspiration (PET), precipitation (P), normalized difference vegetation index (NDVI), and sea surface temperature (SST) anomalies were investigated through statistical analysis of modeled and remote sensing data. It was shown that scPDSIPM and scPDSITH differed in the representation of drought characteristics over SSA. The regional trend magnitudes of scPDSI in SSA were 0.69 (scPDSIPM) and 0.2 mm/decade (scPDSITH), with a difference in values attributed to the choice of PET measuring method used. The scPDSI and remotely sensed-based anomalies of P and NDVI showed wetting and drying trends over the period 1980–2012 with coefficients of trend magnitudes of 0.12 mm/decade (0.002 mm/decade). The trend analysis showed increased drought events in the semi-arid and arid regions of SSA over the same period. A correlation analysis revealed a strong relationship between the choice of PET measuring method and both P and NDVI anomalies for monsoon and pre-monsoon seasons. The correlation analysis of the choice of PET measuring method with SST anomalies indicated significant positive and negative relationships. This study has demonstrated the applicability of multiple data sources for drought assessment and provides useful information for regional drought predictability and mitigation strategies