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Attributing sources of tropical cyclogenesis (TCG) bias to large‐scale circulation in global circulation models is challenging. Here, we propose the use of empirical orthogonal functions as an approach to understand model bias of TCG. Two leading modes of large‐scale wind circulations in the West Pacific can explain the TCG frequency and location in both climate reanalysis and the MetUM model. In the reanalysis, the two modes distinguish the summer monsoon trough position and the strength of the north Pacific subtropical high. However, in the model, the wind circulations are biased toward the positive phase of simulated modes thus overestimating TCG in the entire Main Development Region. This bias is further related to the north‐eastward shifted monsoon trough and a weakened subtropical high, and overly strong tropics‐subtropics connections. This approach could be deployed more widely to other basins and models to diagnose the causes of TCG bias. Plain Language Summary: General Circulation Models (GCMs) have bias in tropical cyclogenesis (TCG). This bias largely reduces the credibility of GCMs. In this study, we suggest an empirical orthogonal function‐based approach to trace the sources of TCG bias in the western North Pacific more rigorously back to large‐scale wind circulation patterns. We find that TCG bias in both basin‐wide and regional scales is a combination of the two biased circulation patterns, associated with too strong tropics‐subtropics connections. The two biased circulation patterns are also featured with a north‐eastward shifted monsoon trough and a weakened subtropical high. We choose the Met Office climate models to demonstrate the approach. But, it can be applied to other GCMs and basins to better understand TCG bias and inform further model development. Key Points: Multivariate empirical orthogonal functions are used to diagnose model bias of tropical cyclogenesis (TCG) in the West PacificTwo leading modes of large‐scale wind circulations can explain most of TCG variability in both ECMWF fifth generation climate reanalysis and MetUMIn MetUM, positive TCG bias is related to large‐scale wind circulations biased toward the positive phase of the simulated modes [ABSTRACT FROM AUTHOR]

In this study, we examined the sensitivity of tropical cyclone (TC) characteristics and precipitation to the Cordillera Mountain Range (CMR) in Luzon, Philippines. Using the Weather Research and Forecasting (WRF) model, we simulated eight TCs with four different CMR orographic elevations: Control, Flat, Reduced, and Enhanced. We found that at significance level α = 0.05 , TC intensity significantly weakened as early as 21 h prior to landfall in the Enhanced experiment relative to the Control, whereas there was little change in the Flat and Reduced experiments. However, throughout the period when the TC crossed Luzon, we found no significant differences for TC movement speed and position in the different orographic elevations. When a TC made landfall, associated precipitation over the CMR increased as the mountain height increased. We further investigated the underpinning processes relevant to the effect of the CMR on precipitation by examining the effects of mountain slope, incoming perpendicular wind speed, and the moist Froude Number ( F w ). Compared with other factors, TC precipitation was most strongly correlated with the strength of approaching winds multiplied by the mountain slope, i.e., stronger winds blowing up steeper mountain slopes caused higher amounts of precipitation. We also found that individually, F w , mountain height, and upslope wind speeds were poor indicators of orographically induced precipitation. The effects of mountain range on TC rainfall varied with TC cases, highlighting the complexity of the mountain, wind, and rainfall relationship. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) seriously threaten the safety of human life and property especially when approaching a coast or making landfall. Robust, long-lead predictions are valuable for managing policy responses. However, despite decades of efforts, seasonal prediction of TCs remains a challenge. Here, we introduce a deep-learning prediction model to make skillful seasonal prediction of TC track density in the Western North Pacific (WNP) during the typhoon season, with a lead time of up to four months. To overcome the limited availability of observational data, we use TC tracks from CMIP5 and CMIP6 climate models as the training data, followed by a transfer-learning method to train a fully convolutional neural network named SeaUnet. Through the deep-learning process (i.e., heat map analysis), SeaUnet identifies physically based precursors. We show that SeaUnet has a good performance for typhoon distribution, outperforming state-of-the-art dynamic systems. The success of SeaUnet indicates its potential for operational use. [ABSTRACT FROM AUTHOR]

A new regional coupled modelling framework is introduced – the Regional Coupled Suite (RCS). This provides a flexible research capability with which to study the interactions between atmosphere, land, ocean, and wave processes resolved at kilometre scale, and the effect of environmental feedbacks on the evolution and impacts of multi-hazard weather events. A configuration of the RCS focussed on the Indian region, termed RCS-IND1, is introduced. RCS-IND1 includes a regional configuration of the Unified Model (UM) atmosphere, directly coupled to the JULES land surface model, on a grid with horizontal spacing of 4.4 km, enabling convection to be explicitly simulated. These are coupled through OASIS3-MCT libraries to 2.2 km grid NEMO ocean and WAVEWATCH III wave model configurations. To examine a potential approach to reduce computation cost and simplify ocean initialization, the RCS includes an alternative approach to couple the atmosphere to a lower resolution Multi-Column K-Profile Parameterization (KPP) for the ocean. Through development of a flexible modelling framework, a variety of fully and partially coupled experiments can be defined, along with traceable uncoupled simulations and options to use external input forcing in place of missing coupled components. This offers a wide scope to researchers designing sensitivity and case study assessments. Case study results are presented and assessed to demonstrate the application of RCS-IND1 to simulate two tropical cyclone cases which developed in the Bay of Bengal, namely Titli in October 2018 and Fani in April 2019. Results show realistic cyclone simulations, and that coupling can improve the cyclone track and produces more realistic intensification than uncoupled simulations for Titli but prevents sufficient intensification for Fani. Atmosphere-only UM regional simulations omit the influence of frictional heating on the boundary layer to prevent cyclone over-intensification. However, it is shown that this term can improve coupled simulations, enabling a more rigorous treatment of the near-surface energy budget to be represented. For these cases, a 1D mixed layer scheme shows similar first-order SST cooling and feedback on the cyclones to a 3D ocean. Nevertheless, the 3D ocean generally shows stronger localized cooling than the 1D ocean. Coupling with the waves has limited feedback on the atmosphere for these cases. Priorities for future model development are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we examine the problem of optimal state estimation or filtering in stochastic systems using an approach based on information theoretic measures. In this setting, the traditional minimum mean-square measure is compared with information theoretic measures, Kalman filtering theory is reexamined, and some new interpretations are offered. We show that for a linear Gaussian system, the Kalman filter is the optimal filter not only for the mean-square error measure, but for several information theoretic measures which are introduced in this work. For nonlinear systems, these same measures generally are in conflict with each other, and the feedback control policy has a dual role with regard to regulation and estimation. For linear stochastic systems with general noise processes, a lower bound on the achievable mutual information between the estimation error and the observation are derived. The properties of an optimal (probing) control law and the associated optimal filter, which achieve this lower bound, and their relationships are investigated. It is shown that for a linear stochastic system with an affine linear filter for the homogeneous system, under some reachability and observability conditions, zero mutual information between estimation error and observations can be achieved only when the system is Gaussian. Index Terms - Dual control, entropy, Kalman filtering, state estimation.

In this paper, new results for estimating the solution of differential and algebraic Lyapunov matrix equations are obtained, and some of the well-known results are generalized. Index Terms - Inequality, Lyapunov equations, matrix measure, trace.

In this paper the effect of state quantization in scalar discrete-time linear control systems is studied by analyzing the system as a partially observed stochastic system. The problem of optimal state information gathering and filtering is investigated using information theoretic measures and formulating the state estimation problem as an entropy optimization problem. The active probing effect of the feedback control is thoroughly studied. Optimal feedback controls which minimize various types of entropy costs are determined, and it is shown that this problem is equivalent to an optimal control problem for a controlled Markov chain.

The European Centre for Medium-range Weather Forecasts (ECMWF) has produced the ocean-atmosphere coupled reanalysis for the twentieth century, CERA-20C, following on from the similar but atmosphere-only reanalysis ERA-20C. Here we demonstrate the capability of CERA-20C in producing more physically consistent ocean and atmosphere boundary conditions, by focusing on sea surface temperature (SST)-precipitation intraseasonal relationships. CERA-20C reproduces well the observed SST-precipitation correlations, while these relationships are poorly represented in ERA-20C, with the greatest discrepancies in the early 1900s. The improved relationships in CERA-20C are due to intraseasonal improvements in SST that are not present in the external HadISST2 product. In CERA-20C, SST-precipitation relationships are slightly weaker in the 1900s than in the 2000s, mainly due to differences in the assimilated observation density. We also find that the coupled model initialized from CERA-20C in the 2000s realistically simulates these relationships, while relaxing SST toward HadISST2 tends to damp these relationships. CERA-20C has improved mean and variance in precipitation over ERA-20C, but these are mostly due to improvements in the atmospheric model and not due to coupled feedbacks. [ABSTRACT FROM AUTHOR]

In this note, a counterexample to the main result given in [1] is constructed. We generalize a well-known result and obtain better bounds for the trace of two matrices.

Sea level has risen significantly in the recent decades and is expected to rise further based on recent climate projections. Ocean reanalyses that synthetize information from observing networks, dynamical ocean general circulation models, and atmospheric forcing data offer an attractive way to evaluate sea level trend and variability and partition the causes of such sea level changes at both global and regional scales. Here, we review recent utilization of reanalyses for steric sea level trend investigations. State-of-the-science ocean reanalysis products are then used to further infer steric sea level changes. In particular, we used an ensemble of centennial reanalyses at moderate spatial resolution (between 0.5 × 0.5 and 1 × 1 degree) and an ensemble of eddy-permitting reanalyses to quantify the trends and their uncertainty over the last century and the last two decades, respectively. All the datasets showed good performance in reproducing sea level changes. Centennial reanalyses reveal a 1900–2010 trend of steric sea level equal to 0.47 ± 0.04 mm year−1, in agreement with previous studies, with unprecedented rise since the mid-1990s. During the altimetry era, the latest vintage of reanalyses is shown to outperform the previous ones in terms of skill scores against the independent satellite data. They consistently reproduce global and regional upper ocean steric expansion and the association with climate variability, such as ENSO. However, the mass contribution to the global mean sea level rise is varying with products and its representability needs to be improved, as well as the contribution of deep and abyssal waters to the steric sea level rise. Similarly, high-resolution regional reanalyses for the European seas provide valuable information on sea level trends, their patterns, and their causes. [ABSTRACT FROM AUTHOR]