The Spatiotemporal Evolution of the Diurnal Cycle in Two WRF Simulations of Tropical Cyclones

The properties of diurnal variability in tropical cyclones (TCs) and the mechanisms behind them remain an intriguing aspect of TC research. This study provides a comprehensive analysis of diurnal variability in two simulations of TCs to explore these mechanisms. One simulation is a well known Hurricane Nature Run, which is a realistic simulation of a TC produced using the Weather Research and Forecasting model (WRF). The other simulation is a realistic simulation produced using WRF of Hurricane Florence (2018) using hourly ERA5 reanalysis data as input. Empirical orthogonal functions and Fourier filtering are used to analyze diurnal variability in the TCs. In both simulations a diurnal squall forms at sunrise in the inner core and propagates radially outwards and intensifies until midday. At midday the upper-level outflow strengthens, surface inflow weakens, and the cirrus canopy reaches its maximum height and radial extent. At sunset and overnight, the surface inflow is stronger, and convection inside the RMW peaks. Therefore, two diurnal cycles of convection exist in the TCs with different phases of maxima: eyewall convection at sunset and at night, and rainband convection in the early morning. This study finds that the diurnal pulse in the cirrus canopy is not advectively-driven, nor can it be attributed to weaker inertial stability at night; rather, the results indicate direct solar heating as a mechanism for cirrus canopy lifting and enhanced daytime outflow. These results show a strong diurnal modulation of tropical cyclone structure, and are consistent with other recent observational and modeling studies of the TC diurnal cycle.

Long range forecasting parameters vis-a-vis sub-divisional summer rainfall over India

Interannual variability of Empirical Orthogonal Functions (EOF) based upon regional/global parameters, associated with the summer monsoon rainfall over different meteorological sub-divisions of the country have been discussed, based upon the data during the years 1958 to 1990 enabling us to identify three broad sub-divisions of the country. It was interesting to note that the first empirical orthogonal function did not show significant correlation with monsoon rainfall over most SUB-DIVISIONS of the NE and SE parts of the country. However, this EOF was found to be significantly correlated with the rainfall over the remaining meteorological sub-divisions of the country.

EMPIRICAL ORTHOGONAL FUNCTIONS ASSOCIATED WITH PARAMETERS USED IN LONG RANGE FORECASTING OF INDIAN SUMMER MONSOON

EEmpirical Orthogonal Functions (EOF),. associated with the; parameters for long range forecasting of Indian summer monsoon onset and seasonal. rainfall have been discussed. It was found that the percentage of variance explained was 77 and 67 respectively through the first four EOF. The highest correlation coefficient with the onset date was found for the first function which showed the maximum influence of Cobar (Australia) and Darwin (Australia) zonal winds on the onset date. It was interesting to note that for rainfall prediction predominant effect on the first EOF was noticed of 50 hPa ridge over northern hemisphere, Eurasian snow cover, Argentina pressure (negatively correlated) and 500 hpa ridge, 10 hPa Balboa wind, north, central India and east coast minimum temperatures, and northern hemisphere temperature. However, the Influence of EI-Nino, equatorial pressure and Darwin pressure (Including Tahiti minus Darwin) and Himalayan snow cover was almost negligible. The eigen index for the onset date suggests a complementary method for its application In long range prediction of summer monsoon onset date.

A pseudoproxy assessment of why climate field reconstruction methods perform the way they do in time and space

Spatiotemporal paleoclimate reconstructions that seek to estimate climate conditions over the last several millennia are derived from multiple climate proxy records (e.g., tree rings, ice cores, corals, and cave formations) that are heterogeneously distributed across land and marine environments. Assessing the skill of the methods used for these reconstructions is critical as a means of understanding the spatiotemporal uncertainties in the derived reconstruction products. Traditional statistical measures of skill have been applied in past applications, but they often lack formal null hypotheses that incorporate the spatiotemporal characteristics of the fields and allow for formal significance testing. More recent attempts have developed assessment metrics to evaluate the difference of the characteristics between two spatiotemporal fields. We apply these assessment metrics to results from synthetic reconstruction experiments based on multiple climate model simulations to assess the skill of four reconstruction methods. We further interpret the comparisons using analysis of empirical orthogonal functions (EOFs) that represent the noise-filtered climate field. We demonstrate that the underlying features of a targeted temperature field that can affect the performance of CFRs include the following: (i) the characteristics of the eigenvalue spectrum, namely the amount of variance captured in the leading EOFs; (ii) the temporal stability of the leading EOFs; (iii) the representation of the climate over the sampling network with respect to the global climate; and (iv) the strength of spatial covariance, i.e., the dominance of teleconnections, in the targeted temperature field. The features of climate models and reconstruction methods identified in this paper demonstrate more detailed assessments of reconstruction methods and point to important areas of testing and improving real-world reconstruction methods.

Rainfall variability and meteorological drought in the Horn of Africa

Basic rainfall characteristics and drought over the Horn of Africa (HoA) is investigated, from 1901 to 2010. Standard Precipitation Index (SPI) is used to study drought variability, mainly focusing on 3-month SPI. The dominant mode of variability of seasonal rainfall was analyzed by performing Empirical orthogonal functions (EOF) analysis. Gridded data is sourced from Climate Research Unit (CRU), spanning from 1901 to 2010. The HoA experiences predominantly bimodal rainfall distribution in time; March to May (MAM) and October to December (OND). The spatial component of the first eigenvector (EOF1) shows that the MAM and OND seasonal rainfalls are dominated by negative and positive loadings, respectively. The EOF1 explain 34.5% and 58.9% variance of MAM and OND seasonal rainfall, respectively. The EOF2, 3 and 4 are predominantly positive, explaining less than 25% in total of the seasonal rainfall variance in the two seasons. The last two decades experienced the highest negative anomaly, with OND seasonal rainfall showing higher anomalies as compared to MAM season. The OND season recorded 9% more drought events as compared to MAM season. The frequency of occurrence of moderate, severe and extreme dryness was almost the same in the two seasons. These results give a good basis for regional model validation, as well as mapping out drought hotspots and projections studies in the HoA.

DINEOF reconstruction for creating long-term cloud-free sea surface temperature data records: A Case study in Lombok Strait, Indonesia

A long-term reliable sea surface temperature (SST) satellite data record is requisite resources for monitoring to understand climate variability. Creating a long-term data record especially for climate variability requires a combination of multiple satellite products. Consequently, missing data issues are inevitable. Hence, DINEOF (Data Interpolating Empirical Orthogonal Functions) has been applied to attain a complete and coherent multi-sensor SST data record with EOF-based technique by reconstructing the missing data. Unfortunately, the technique can lead to large discontinuities in the data reconstruction due to images depiction within long time series data. For that reason, filtering the temporal covariance matrix had been applied to reduce the spurious variability and more realistic reconstructions are obtained. However, this approach has not yet tested in tropical region with higher evaporation which cause incomplete satellite image coverage. Therefore, the objective of this research is to reconstruct SST of Lombok strait with data gaps up to 58.16% in one year. It is successfully reconstructed until the last iteration of 42 optimal EOF modes with the convergence achieved up to 0.9806×10-3, including previous set-aside data for internal cross-validation. The results highlight that the DINEOF method can effectively reconstruct SST data in Lombok Strait.

Modes of the BiOS-driven Adriatic Sea thermohaline variability

In this study the impact of the Adriatic-Ionian Bimodal Oscillating System (BiOS) on the interannual to decadal variability of the Adriatic Sea thermohaline circulation is quantified during the 1987-2017 period with the numerical results of the Adriatic Sea and Coast (AdriSC) historical kilometer-scale climate simulation. The time series associated with the first five Empirical Orthogonal Functions (EOFs) computed from the salinity, temperature and current speed monthly detrended anomalies at 1-km resolution are correlated to the BiOS signal. First, it is found that the AdriSC climate model is capable to reproduce the BiOS-driven phases derived from in-situ observations along a long-term monitoring transect in the middle Adriatic. Then, for the entire Adriatic basin, high correlations to the 2-year delayed BiOS signal are obtained for the salinity and current speed first two EOF time series at 100 m depth and the sea-bottom Finally, the physical interpretation of the EOF spatial patterns reveals that Adriatic bottom temperatures are more influenced by the dense water circulation than the BiOS. These findings confirmed and generalized the known dynamics derived previously from observations, and the AdriSC climate model can thus be used to better understand the past and future BiOS-driven physical processes in the Adriatic Sea.

Spatio-Temporal Variations of Precipitable Water Vapor and Horizontal Tropospheric Gradients from GPS during Typhoon Lekima

GPS data during Typhoon Lekima at 700 stations in China were processed by the Precise Point Positioning (PPP) method. A refined regional Tm model was used to derive the precipitable water vapor (PWV) at these GPS stations. Spatio-temporal variations of PWV with the typhoon process were analyzed. As the typhoon approached, PWV at stations near the typhoon center increased sharply from about 50 mm to nearly 80 mm and then dropped back to about 40–50 mm as the typhoon left. Comparisons of GPS, radiosonde, the Global Data Assimilation System (GDAS) Global Forecast System (GFS) analysis products and ERA5 reanalysis products at four matched GPS-RS stations show overall overestimations of PWV from radiosonde, GFS and ERA5 compared with GPS in a statistical perspective. An empirical orthogonal functions (EOF) analysis of the PWV during the typhoon event revealed some different patterns of variability, with both the first EOF (~36.1% of variance) and second EOF (~30.3% of variance) showing distinctively large anomalies over the typhoon landing locations. The typhoon caused a large horizontal tropospheric gradient (HTG) with the magnitude reaching 5 mm and the direction pointing to the typhoon center when it made a landfall on mainland China. The magnitude and the consistency of the HTG direction decreased overall as the typhoon weakened.

Dipole Pattern of Meridional Atmospheric Internal Energy Transport Across the Arctic Gate

High-latitude atmospheric meridional energy transport plays a fundamental role in the Arctic climate system. However, despite numerous studies, there are no established clear regional features of the atmospheric energy transport components from a large-scale perspective. This study aims at investigating the internal energy and its instantaneous sensible and latent heat transports in the troposphere through the Arctic gate at 70°N using the high-resolution climate reanalysis ERA5. We have done a regional analysis of the time series of heat fluxes across the zonal section and found by decomposing them into the empirical orthogonal functions that they have opposing features for the Eastern and Western Hemispheres. In particular, the sensible heat transport dominates in the Western Hemisphere, whereas the latent heat transport dominates in the Eastern Hemisphere. Moreover, we detected the existence of an anti-phase dipole pattern for each of these components in the entire troposphere, which is robust because it was retained during both the climate cooling in 1950–1978 and warming in 1979–2019. The hemispheric net fluxes indicate that the Arctic gains internal energy mostly due to the latent heat transport.