An analysis of historical Marine Heatwaves over the Australian region based on multiple satellite observation products

Marine heatwaves (MHWs) have recently been recognized as extreme events considering their devastating impacts on marine ecosystems. Both the common and differing characteristics of historical MHW events may contribute to future studies and predictions of regional MHWs globally. We firstly compare five high-resolution Sea Surface Temperature (SST) climatology data sets around the Australian coast to investigate the uncertainty and dependency introduced by the reference SSTs to current estimates of SST anomalies. The results indicate that Climate Change Initiative (CCI) and SST Atlas of the Australian Regional Seas (SSTAARS) climatologies would be suitable for use as a reference over the Australian region. We then analyze the main spatial and temporal features of historical MHW events on a pixel-wise scale over the Australian region by forming MHW metrics using CCI and Copernicus Climate Change Service (C3S) Level 4 (L4) analyses from 1981 to 2020. Relatively short-term events (<10 days) account for over half of the identified MHWs over the domain, among which nearly 90% are classified as having moderate intensity. Natural variability of the local climate system contributes to most of these short-term and less intense events, while the MHWs may emerge under the continuing warming of the ocean surface. Purposefully excluding the short-term events would benefit the long-term studies of MHWs, by highlighting the variations and impacts of the relatively longer and more intense events. Three Australian case study regions are selected: the northwest coastal region (NW region, 17°S-25°S, 110°E-119°E), southwest coastal region (SW region, 25°S-40°S, 110°E-120°E), and southeast coastal region (SETS region) containing the Tasman Sea (33°S-45°S, 147°E-167°E) and southern Tasmania region (43°S-48°S, 142°E-152°E). Multiple SST including CCI L4, IMOS Level 3 Super-Collated (L3S) products, sea level anomaly (SLA) and wind stress products are used to analyze and observe historical MHWs. SLA over the 90th percentile, as a complementary parameter is recommended to identify subsurface MHWs in regions free of strong eddy activities, which could help improve the understanding and predictability of MHWs. We also recommend using both L3S and L4 SST data to identify MHWs when analyzing small-scale spatial variability over regional seas globally if the MHW-mask from the gap-free L4 SST and MHW threshold derived from the L3S data are applied to guarantee the reliability of L3S observations

On the Structure of the Lower Troposphere in the Summertime Stratocumulus Regime of the Northeast Pacific

Data collected in situ as part of the second field study of the Dynamics and Chemistry of Marine Stratocumulus field program are used to evaluate the state of the atmosphere in the region of field operations near 30°N, 120°W during July 2001, as well as its representation by a variety of routinely available data. The routine data include both the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and NCEP–NCAR reanalyses, forecasts from their respective forecast systems (the Integrated and Global Forecast Systems), the 30-km archive from the International Satellite Cloud Climatology Project (ISCCP), the Quick Scatterometer surface winds, and remotely sensed fields derived from radiances measured by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the Advanced Microwave Sounding Unit, and the Advanced Very High Resolution Radiometer. The analysis shows that outside of the boundary layer the state of the lower troposphere is reasonably represented by the reanalysis and forecast products, with the caveat of a slight warm bias at 850 hPa in the NCEP–NCAR products. Within the planetary boundary layer (PBL) the agreement is not as good: both the boundary layer depth and cloud amount are underpredicted, and the boundary layer temperature correlates poorly with the available data, which may be related to a poor representation of SSTs in this region of persistent cloud cover. ERA-40 also suffers from persistently weak zonal winds within the PBL. Among the satellite records the ISCCP data are found to be especially valuable, evincing skill in both predicting boundary layer depth (from cloud-top temperatures and TMI surface temperatures) and cloud liquid water paths (from cloud optical depths). An analysis of interannual variability (among Julys) based on ERA-40 and the 1983–2001 ISCCP record suggests that thermodynamic quantities show similar interannual and synoptic variability, principally concentrated just above the PBL, while dynamic quantities vary much more on synoptic time scales. Furthermore, the analysis suggests that the correlation between stratocumulus cloud amount and lower-tropospheric stability exhibits considerable spatial structure and is less pronounced than previously thought

Climate Modeling, Outgoing Longwave Radiation, and Tropical Cyclone Forecasting

Climate modeling and tropical cyclone forecasting are two significant is- sues that are continuously being improved upon for more accurate weather forecasting and preparedness. In this thesis, we have studied three climate models and formulated a new model with a view to determine the outgoing longwave radiation (OLR) budget at the top of the atmosphere (TOA) as ob- served by the National Oceanic and Atmospheric Administration’s (NOAA) satellite based Advanced Very High Resolution Radiometer (AVHRR). In 2006, Karnauskas proposed the African meridional OLR as an Atlantic hur- ricane predictor, the relation was further proven in 2016 by Karnauskas and Li. Here we have considered a similar study for all other tropical cyclone basins

Spatio-Temporal Analysis of Sea Surface Temperature in the East China Sea Using TERRA/MODIS Products Data

Sea surface temperature (SST) is an important parameter in determining the atmospheric and oceanic circulations, and satellite thermal infrared remote sensing can obtain the SST with very high spatio-temporal resolutions. The study first validated the accuracy of TERRA MODIS SST daytime and nighttime products with the timing SST measurements from the ships in the East China Sea (ECS) in February, May, August and November, 2001, and then the daily variation of daytime and nighttime SST difference was analyzed. Using 16-year MODIS SST monthly products data from February 2000 to January 2016, when all SST monthly products in February, May, August and November were averaged respectively, the seasonal spatial distribution pattern of SST in the ECS was discovered. After monthly sea surface temperature anomaly was finally processed by the empirical orthogonal function (EOF), the interannual variability of SST in the ECS was discussed. The results show that the MODIS SST daily products have a good accuracy with a mean absolute percentage error (MAPE) below 5%. The SST difference between day and night is the largest in winter, followed by spring, then for autumn and the smallest in summer, while the diurnal SST difference is very low for the same season in the different seas. The SST in the ECS displays the obvious seasonal spatial distribution pattern, in which the SST of winter is gradually increasing from north to south, while local temperature difference is the largest for 26.5°C in a year. In comparison, the SST in summer tends uniform and the difference is not more than 5°C in the whole sea. From the EOF analysis of SST anomaly, the interannual variability of SST in the ECS is affected by the East Asian monsoon, the latitudinal difference of solar radiation, the offshore circulation and the submarine terrain

Aerosol Data Sources and Their Roles within PARAGON

We briefly but systematically review major sources of aerosol data, emphasizing suites of measurements that seem most likely to contribute to assessments of global aerosol climate forcing. The strengths and limitations of existing satellite, surface, and aircraft remote sensing systems are described, along with those of direct sampling networks and ship-based stations. It is evident that an enormous number of aerosol-related observations have been made, on a wide range of spatial and temporal sampling scales, and that many of the key gaps in this collection of data could be filled by technologies that either exist or are expected to be available in the near future. Emphasis must be given to combining remote sensing and in situ active and passive observations and integrating them with aerosol chemical transport models, in order to create a more complete environmental picture, having sufficient detail to address current climate forcing questions. The Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON) initiative would provide an organizational framework to meet this goal

Diurnal ocean surface layer model validation

The diurnal ocean surface layer (DOSL) model at the Fleet Numerical Oceanography Center forecasts the 24-hour change in a global sea surface temperatures (SST). Validating the DOSL model is a difficult task due to the huge areas involved and the lack of in situ measurements. Therefore, this report details the use of satellite infrared multichannel SST imagery to provide day and night SSTs that can be directly compared to DOSL products. This water-vapor-corrected imagery has the advantages of high thermal sensitivity (0.12 C), large synoptic coverage (nearly 3000 km across), and high spatial resolution that enables diurnal heating events to be readily located and mapped. Several case studies in the subtropical North Atlantic readily show that DOSL results during extreme heating periods agree very well with satellite-imagery-derived values in terms of the pattern of diurnal warming. The low wind and cloud-free conditions necessary for these events to occur lend themselves well to observation via infrared imagery. Thus, the normally cloud-limited aspects of satellite imagery do not come into play for these particular environmental conditions. The fact that the DOSL model does well in extreme events is beneficial from the standpoint that these cases can be associated with the destruction of the surface acoustic duct. This so-called afternoon effect happens as the afternoon warming of the mixed layer disrupts the sound channel and the propagation of acoustic energy

Interannual SST variability in the Japan/East Sea and relationship with environmental variables

Journal of Oceanography, Oceanographic Society of Japan, 62, 115-132

Assessing River Ice Breakup Date, Coastal Tundra Vegetation And Climate Divisions In The Context Of Alaska Climate Variability

Thesis (Ph.D.) University of Alaska Fairbanks, 2012In Alaska, there exists a substantial knowledge gap of key climate drivers and filling these gaps is vital since life and the economy are inexorably linked with climate in the state. This thesis identifies and investigates three topics that advance the understanding of Alaska climate variability: the role of large-scale climate in Interior river ice breakup, the link between climate and arctic tundra vegetation, and climate divisions based on objective methods. River ice breakup in the Yukon-Kuskoswim watershed is occurring earlier by 1.3 days decade-1 1948-2008 and displays large year-to-year variability. April-May Interior Alaska air temperatures are the best predictor of river ice breakup and were linked to El Nino Southern Oscillation (ENSO). During the warm phase of ENSO, fewer storms track into the Gulf of Alaska during Boreal Spring, resulting in reduced April-May cloudiness over Alaska, increased solar insolation at the land surface, warmer air temperatures and consequently earlier breakup. Northern Alaska tundra vegetation productivity has increased 1982-2011, based on the Normalized Difference Vegetation Index (NDVI), a satellite measure of vegetation correlated with above ground biomass. Vegetation productivity was linked to the Beaufort High circulation as well as snowfall, in addition to land surface temperatures and coastal sea ice extent. NDVI has decreased from 1982-2011 over the coastal tundra along the Bering Sea and was correlated with delayed springtime warming due to enhanced coastal sea ice and a delayed snowmelt. Cluster analysis was applied to 2-meter air temperature data 1977-2010 at meteorological stations to construct climate divisions for Alaska. Stations were grouped together objectively based on similar homogeneous seasonal and annual climate variability and were refined using local expert knowledge to ultimately identify 13 divisions. Correlation analysis using gridded downscaled temperature and precipitation data validated the final division lines and documented that each division has similar a similar annual cycle in temperature and precipitation. Overall, this work documented substantial links and identified mechanisms joining the large-scale climate to that of Alaska. A better understanding of the role of large-scale climate variability in river ice breakup or tundra greening holds promise for developing seasonal and longer-term forecasts