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Ke Wang
and
Xinyu Lyu

Abstract

The mesoscale features of 400 tropical cyclones (TCs) over the western North Pacific during 2014–2021 were investigated through Global Precipitation Measurement Microwave Imager and Dual-frequency Precipitation Radar observations. An improved algorithm has been developed to identify active mesoscale convective systems (MCSs) in TCs. These MCSs are continuous systems with active precipitation, exhibiting deep convection, and only account for a small portion of the precipitating pixels within a TC. This study highlights the evolving characteristics of MCSs and their precipitation features in TCs of different intensity categories. The development and evolution of active MCSs in TCs are influenced by the degree of organization of the system. During the tropical depression stage, the maintenance of MCSs relies heavily on substantial convective precipitation pixels, especially deep convection. The convection exhibits a high echo top height and significant ice scattering feature. As the TC intensity increases, MCSs are more likely to be in a mature state where convective precipitation is no longer as crucial for their development. This transition leads to a decrease in the proportion and the 20-dBZ echo top height of deep convection in both the inner- and outer-core region. The proportion of stratiform precipitation increases, ultimately resulting in an expansion in the MCS size and an increase in the PCTs of MCSs. By elucidating these phenomena, this study deepens our understanding of the structure and precipitation features of TCs, providing insights into the unique characteristics of MCS development that differ from the synoptic-scale features of TC development.

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Han Zhang
,
Xin-Zhong Liang
,
Yongjiu Dai
,
Lianchun Song
,
Qingquan Li
,
Fang Wang
, and
Shulei Zhang

Abstract

This study investigates skill enhancement in operational seasonal forecasts of Beijing Climate Center’s Climate System Model through regional Climate-Weather Research and Forecasting (CWRF) downscaling and improved land initialization in China. The downscaling mitigates regional climate biases, enhancing precipitation pattern correlations by 0.29 in spring and 0.21 in summer. It also strengthens predictive capabilities for interannual anomalies, expanding skillful temperature forecast areas by 6% in spring and 12% in summer. Remarkably, during seven of ten years with relative high predictability, the downscaling increases average seasonal precipitation anomaly correlations by 0.22 and 0.25. Additionally, substitution of initial land conditions via a Common Land Model integration reduces snow cover and cold biases across the Tibetan Plateau and Mongolia-Northeast China, consistently contributing to CWRF’s overall enhanced forecasting capabilities.

Improved downscaling predictive skill is attributed to CWRF’s enhanced physics representation, accurately capturing intricate regional interactions and associated teleconnections across China, especially linked to the Tibetan Plateau’s blocking and thermal effects. In summer, CWRF predicts an intensified South Asian High alongside a strengthened East Asian Jet compared to CSM, amplifying cold air advection and warm moisture transport over central to northeast regions. Consequently, rainfall distributions and interannual anomalies over these areas experience substantial improvements. Similar enhanced circulation processes elucidate skill improvement from land initialization, where accurate specification of initial snow cover and soil temperature within sensitive regions persists in influencing local and remote circulations extending beyond two seasons. Our findings emphasize the potential of improving physics representation and surface initialization to markedly enhance regional climate predictions.

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Tonya R. Haigh
,
Douglas R. Kluck
,
Dennis P. Todey
, and
Laurie Nowatzke

Abstract

Evaluation of near-term (sub)seasonal climate services’ impact is challenging but necessary for ensuring that society’s needs for actionable information are met. We use a descriptive study of the monthly North Central Climate and Drought Webinar Series at two time points (2014 and 2021) to examine societal impacts on capacity-building, sense-making, fact-establishing, communication, decision-making, and social-ecological systems. The North Central Climate and Drought Webinar Series arose following a 2011 climate disaster and established itself over the next ten years as a monthly resource for climate and impact information translation and interaction. Survey respondents indicated early benefits related to understanding how to find and use climate information and improved conceptual understanding of climate issues and problems. Many used webinar information to compare with other sources of data or to incorporate into their own communications, uses which can increase overall societal trust in climate information over time. Attendees’ self-reported capacity for using climate information in decision-making and actual use of information in specific decisions or management context increased as the webinar series approached the ten-year mark. Most participants did not note financial or other social-ecological outcomes of their use of the webinars. We conclude by recommending that climate services be evaluated over sufficiently long time periods to capture evolving impacts, and that evaluations incorporate impact rubrics that measure subtle yet important societal capacities and decision-making processes related to climate risk management.

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Zhuo Wang
,
Mingshi Yang
,
John E. Walsh
,
Robert M. Rauber
, and
Melinda Peng

Abstract

The intensity evolution of Arctic Cyclones (ACs) is examined via cyclone parameter space and composite analyses based on approximately 18,000 AC tracks during 1979-2021. Cyclone parameter spaces are defined by various parameters representing cyclone structure and physical processes relevant to cyclone development. It is shown that intensifying ACs are associated with diabatic heating and characterized by a cold core in both the lower and upper troposphere, as well as a thermally asymmetric and vertically tilted structure. In contrast, the decay phase is associated with diabatic cooling and characterized by a vertically aligned cyclone with reduced horizontal asymmetry. The cyclone parameter space analysis also indicates a warm core in the lower troposphere for a subset of ACs, which may reflect a frontal occlusion. The transition from AC intensification to decay, on average, is marked by a sharp decrease in both upward motion and diabatic heating, along with the vertical alignment of the cyclone structure. Following this transition, an upright cyclone may persist for a long time due to the weak background vertical wind shear, diabatic cooling, and weak Rossby wave energy dispersion. The evolution of ACs can thus be regarded as a two-stage process: a baroclinic development stage aided by diabatic heating, during which the AC evolution may conform with the Norwegian model for midlatitude cyclones, and a slow decay of an equivalent barotropic cyclone, which may leave a remnant tropopause polar vortex after the erosion of the surface circulation.

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Nazanin Chaichitehrani
,
Ruoying He
, and
Mohammad Nabi Allahdadi

Abstract

This study introduces an ensemble learning model for the prediction of significant wave height and average wave period in stations along the U.S. Atlantic coast. The model utilizes the stacking method, combining three base learner models - Lasso regression, support vector machine, and Multi-layer Perceptron - to achieve more precise and robust predictions. To train and evaluate the models, a twenty-year dataset comprising meteorological and wave data was used, enabling forecasts for significant wave height and average wave period at 1, 3, 6, and 12 hour intervals. The data collection involved two NOAA buoy stations situated on the U.S. Atlantic coast. The findings demonstrate that the ensemble learning model constructed through the stacking method yields significantly higher accuracy in predicting significant wave height within the specified time intervals.

Moreover, the study investigates the influence of swell waves on forecasting significant wave height and average wave period. Notably, the inclusion of swell waves improves the accuracy of the 12-hour forecast. Consequently, the developed ensemble model effectively estimates both significant wave height and average wave period. The ensemble model outperforms the individual models in forecasting significant wave height and average wave period. This ensemble learning model serves as a viable alternative to conventional coastal models for predicting wave parameters.

Open access
Florence L. Beaudry
,
Stéphane Bélair
,
Julie M. Thériault
,
Dikra Khedhaouiria
,
Franck Lespinas
,
Daniel Michelson
,
Pei-Ning Feng
, and
Catherine Aubry

Abstract

The Canadian Precipitation Analysis (CaPA) system provides near-real-time precipitation analyses over Canada by combining observations with short-term numerical weather prediction forecasts. CaPA’s snowfall estimates suffer from the lack of accurate solid precipitation measurements to correct the first guess estimate. Weather radars have the potential to add precipitation measurements to CaPA in all seasons but are not assimilated in winter due to radar snowfall estimate imprecision and lack of precipitation gauges for calibration. The main objective of this study is to assess the impact of assimilating Canadian dual-polarized radar-based snowfall data in CaPA to improve precipitation estimates. Two sets of experiments were conducted to evaluate the impact of including radar snowfall retrievals, one set using the high-resolution CaPA (HRDPA) with the currently operational quality control configuration and another increasing the number of assimilated surface observations by relaxing quality control. Experiments spanned two winter seasons (2021 and 2022) in central Canada, covering part of the entire CaPA domain. The results showed that the assimilation of radar-based snowfall data improved CaPA’s precipitation estimates 81.75% of the time for 0.5 mm precipitation thresholds. An increase of the probability of detection together with a decrease in false alarm ratio suggested an improvement of the precipitation spatial distribution and estimation accuracy. Additionally, the results showed improvements for both precipitation mass and frequency biases for low precipitation amounts. For larger thresholds, the frequency bias was degraded. The results also indicated that the assimilation of dual-polarization radar data is beneficial for the two CaPA configurations tested in this study.

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Viktor Gouretski
,
Fabien Roquet
, and
Lijing Cheng

Abstract

The study focuses on biases in ocean temperature profiles obtained by means of Satellite-relayed Data Loggers (SRDL recorders) and Time-Depth temperature recorders (TDR) attached to marine mammals. Quasi-collocated profiles from Argo floats and from ship-based CTD profilers are used as reference. SRDL temperature biases depend on the sensor type and vary with depth. For the most numerous group of VP3 and CTF sensors the bias is negative except for the layer 100-200 m. The vertical bias structure suggests a link to the upper ocean thermal structure within the upper 200 m layer. Accounting for a time lag which might remain in the post-processed data reduces the bias variability throughout the water column. Below 200 m depth the bias remains negative with the overall mean of −0.027±0.07°C. The suggested depth and thermal corrections for biases in SRDL data are within the uncertainty limits declared by the manufacturer. TDR recorders exhibit a different bias pattern, showing the predominantly positive bias of 0.08 to 0.14°C below 100 m primarily due to the systematic error in pressure.

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Suqin Q. Duan
,
Karen A. McKinnon
, and
Isla R. Simpson

Abstract

Climate change projections show amplified warming associated with dry conditions over tropical land. We compare two perspectives explaining this amplified warming: one based on tropical atmospheric dynamics, and the other focusing on soil moisture and surface fluxes. We first compare the full spatiotemporal distribution of changes in key variables in the two perspectives under a quadrupling of CO2 using daily output from the CMIP6 simulations. Both perspectives center around the partitioning of the total energy/energy flux into the temperature and humidity components. We examine the contribution of this temperature/humidity partitioning in the base climate and its change under warming to rising temperatures by deriving a diagnostic linearized perturbation model that relates the magnitude of warming to (1) changes in the total energy/energy flux, (2) the base-climate temperature/humidity partitioning, and (3) changes in the partitioning under warming. We show that the spatiotemporal structure of warming in CMIP6 models is well predicted by the inverse of the base-climate partition factor, which we term the base-climate sensitivity: conditions that are drier in the base climate have a higher base-climate sensitivity and experience more warming. On top of this relationship, changes in the partition factor under intermediate (between wet and dry) surface conditions further enhance or dampen the warming. We discuss the mechanistic link between the two perspectives by illustrating the strong relationships between lower tropospheric temperature lapse rates, a key variable for the atmospheric perspective, and surfaces fluxes, a key component of the land surface perspective.

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