Impact of internal climate variability on the relationship between spring northern tropical Atlantic SST anomalies and succedent winter ENSO: the role of the North Pacific Oscillation

Previous studies suggested that spring sea surface temperature anomalies (SSTAs) in the northern tropical Atlantic (NTA) have a marked influence on the succedent winter El Niño-Southern Oscillation (ENSO). In this study, we examine the spring NTA SSTA-winter ENSO connection in a 50-member large ensemble simulation conducted with the Canadian Centre for Climate Modeling and Analysis second generation Canadian Earth System Model (CanESM2) and a 100-member ensemble simulation conducted with the Max Planck Institute Earth System Model (MPI-ESM). The observed out-of-phase relation of spring NTA SSTA with winter ENSO can be captured by the multi-member ensemble means of the large ensemble simulations from both models. However, the relation shows a large diversity among different ensemble members attributing to the internal climate variability. The preceding winter North Pacific Oscillation (NPO) is suggested to be an important source of the internal climate variability that modulates the spring NTA SSTA-ENSO connection. The modulation of the winter NPO on the subsequent spring NTA SSTA-winter ENSO relation is seen in both climate modeling and observational datasets. When winter NPO and spring NTA SSTA indices have the same (opposite) sign, the linkage between the spring NTA SSTA and the following winter ENSO tends to be weak (strong). The NPO modulates the spring NTA SSTA-winter ENSO relation mainly via changing the zonal wind anomalies over the tropical western-to-central Pacific induced by the spring NTA SSTA. In addition, our analysis indicates that winter NPO may have a marked effect on the predictability of winter ENSO based on the condition of spring NTA SSTA.

Can the assimilation of water isotopologue observation improve the quality of tropical diabatic heating and precipitation?

The strong coupling between atmospheric circulation, moisture pathways and atmospheric diabatic heating is responsible for most climate feedback mechanisms and controls the evolution of severe weather events. However, diabatic heating rates obtained from current meteorological reanalysis show significant inconsistencies. Here, we theoretically assess with an Observation System Simulation Experiment (OSSE) the potential of the MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) Infrared Atmospheric Sounding interferometer (IASI) mid-tropospheric water isotopologue data for constraining uncertainties in meteorological analysis fields. For this purpose, we use the Isotope-incorporated General Spectral Model (IsoGSM) together with a Local Ensemble Transform Kalman Filter (LETKF) and assimilate synthetic MUSICA IASI isotopologue observations. We perform two experiments consisting each of two ensemble simulation runs, one ensemble simulation where we assimilate conventional observations (temperature, humidity and wind profiles obtained from radiosonde and satellite data) and a second one where we assimilate additionally to the conventional observations the synthetic IASI isotopologue data. In the second experiment, we perform one ensemble simulation where only synthetic IASI isotopologue data are assimilated and another one where no observational data at all are assimilated. The first experiment serves to assess the impact of the IASI isotopologue data additional to the conventional observations and the second one to assess the direct impact of the IASI isotopologue data on the meteorological variables, especially on the heating rates and vertical velocity. The assessment is performed for the tropics in the latitude range from 10° S to 10° N. When the synthetic isotopologue data are additionally assimilated, we derive in both experiments lower Root-Mean Square Deviations (RMSDs) and improved skills with respect to meteorological variables (improvement by about 8–13 %). However, heating rates and vertical motion can only be improved throughout the troposphere when additionally to IASI δD conventional observations are assimilated. When only IASI δD is assimilated the improvement in vertical velocity and heating rate is minor (up to a few percent) and restricted to the mid-troposphere. Nevertheless, these assimilation experiments indicate that IASI isotopologue observations have the potential to reduce the uncertainties of diabatic heating rates and meteorological variables in the tropics and in consequence offer potential for improving meteorological analysis, weather forecasts and climatepredictions in the tropical regions.

Characterisation of the chaotic variability of the regional sea level and its components over 1993-2015 at interannual time scales

<p>Satellite altimetry data have revealed a global mean sea level rise of 3.1 mm/yr since 1993 with large regional sea level trend variability. These remote data highlight complex structures especially in strongly eddying regions. A recent study showed that over 38% of the global ocean area, the chaotic variability that spontaneously emerges from the ocean may hinder the attribution to the atmospheric forcing of regional sea level trends from 1993 to 2015. This study aims at complementing this work by first focusing on the atmospherically-forced and chaotic contributions of regional sea level interannual variability and its components (steric and manometric sea level interannual variability). A global ¼° ocean/sea-ice 50-member ensemble simulation is considered to disentangle the imprints of the atmospheric forcing and of the chaotic ocean variability over 1993-2015. The atmospherically-forced and chaotic interannual variabilities of sea level mainly have a steric origin , except in coastal areas. The chaotic part of the interannual variability of sea level and its components is stronger in the Pacific and Atlantic oceans than in the Indian ocean. The chaotic part of the interannual variability of sea level and of its steric component exceeds 20% over 48% of the global ocean area; this fractional area reduces to 26% for the manometric component. As the chaotic part of the regional sea level interannual variability has a substantial imprint, this study then interested in quantifying the periods when it becomes dominant over the atmospherically-forced contribution. This is assessed using spectral analysis on the ensemble simulation in the frequency domain for the sea level and its steric and manometric components over the global ocean as well as in some basins of interest. This enables us to better characterise and quantify the chaotic ocean variability contribution to regional sea level changes and its components.</p>

The NICAM 3.5km-1024 ensemble simulation: Performance optimization and scalability of NICAM-LETKF on supercomputer Fugaku

<p>In parallel with the new Japanese flagship supercomputer, Fugaku, we have continued improving a nonhydrostatic icosahedral atmospheric model (NICAM). Here, we introduce the results of our system-application co-design since 2014. Fugaku's CPU (A64FX) is based on the Arm instruction-set architecture. This 48-core many-core CPU is equipped with 32GB of HBM2 memory, showing data transfer performance comparable to GPUs. We have implemented kernel-level optimizations to take advantage of Fugaku's high memory performance. Among them, we recognized trade-offs related to ensuring memory locality and parallelism, and register allocation. We improved the application's average arithmetic intensity through detailed loop-by-loop performance measurements and reduced memory pressure by actively using single-precision operations. We also redesigned the data layout and the file I/O component of the ensemble data assimilation (DA) system and achieved good scalability in the atmospheric simulation and DA. We performed a global 3.5km mesh, 1024-member ensemble simulation, and DA using 82% of the Fugaku system (131,072 nodes, 6,291,456 cores). In this world's most massive ensemble DA benchmark experiment, the simulation and the DA achieved 29 PFLOPS and 79 PFLOPS of effective performance.</p>

Ensemble flood simulation for the typical catchment in humid climatic zone by using multiple hydrological models

A good performance of hydrological model for flood simulation is of critical importance for flood forecasting. Taking Yandu River catchment, as the study area, three hydrological models (i.e. Xin'anjiang model, TOPMODEL, artificial neural network model) and a multi-model ensemble simulation method (i.e. entropy-based method) were applied to simulate the hydrological processes of 30 flood events occurring in 1981–1987. The performance of the ensemble members and multi-model ensemble simulation method was evaluated by comparing indicators of Nash-Efficiency coefficient, errors in root mean square, peak occurrence time, and relative errors of flood peak discharge, event runoff depth. Results show that the three hydrological models perform well for hydrological simulation of all 30 storm floods with Nash and Sutcliffe Efficiency coefficient of above 0.75 and relative error of less than 10 %. However, different model exhibits a difference in simulation errors of peak discharge and peak occurrence time. For example, BP model has the smallest error of 3.78 % for peak discharge simulation while that of Xin'anjiang model and TOPMODEL are 20.9 % and 24.7 % respectively. The entropy-based ensemble simulation method improved flood simulation accuracy to some extent for all evaluation criteria comparing to the three hydrological models. It is feasible to apply entropy-based ensemble approach for improving accuracy of flood forecasting in humid regions of China.