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Manishka De Mel, William Solecki, Radley Horton, Ryan Bartlett, Abigail Hehmeyer, Shaun Martin, and Cynthia Rosenzweig

Abstract

Integrating climate risk information into resilience-building activities in the field is important to ensure that adaptation is based on the best available science. Despite this, many challenges exist when developing, communicating, and incorporating climate risk information. There are limited resources on how stakeholders perceive risks, use risk information, and what barriers exist to limit knowledge integration. This paper seeks to define the following: 1) What do conservation stakeholders consider to be the most significant climate risks they face now and possibly in the future? 2) What have been the most significant barriers to their using climate risk information? 3) What sources and types of knowledge would be most useful for these managers to overcome these barriers? A survey was conducted among stakeholders (n = 224) associated with World Wildlife Fund projects in tropical and subtropical countries. A very high proportion of stakeholders used climate risk information and yet faced integration-related challenges, which included too much uncertainty and the lack of a relevant scale for planning. The main factors preventing the use of climate risk information in decision-making were unavailability of climate risk information, no or limited financial or human resources available to respond, lack of organizational mandate or support, and no or limited institutional incentives. Comparing perceived current and future risks revealed a decline in concern for some future climate hazards. Survey respondents identified scientific reports, climate scientists, and online sources as the most useful information sources of climate risk information, while (i) maps and illustrations; (ii) scenarios format; and (iii) data tables, graphs, and charts were identified as user-friendly formats.

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Gary M. Lackmann, Rebecca L. Miller, Walter A. Robinson, and Allison C. Michaelis

Abstract

Persistent anomalies (PAs) are associated with a variety of impactful weather extremes, prompting research into how their characteristics will respond to climate change. Previous studies, however, have not provided conclusive results, owing to the complexity of the phenomenon and to difficulties in general circulation model (GCM) representations of PAs. Here, we diagnose PA activity in 10 years of current and projected future output from global, high-resolution (15-km mesh) time-slice simulations performed with the Model for Prediction Across Scales-Atmosphere (MPAS-A). These time slices span a range of ENSO states. They include high-resolution representations of sea surface temperatures and GCM-based sea ice for present and future climates. Future projections, based on the RCP8.5 scenario, exhibit strong Arctic amplification and tropical upper warming, providing a valuable experiment with which to assess the impact of climate change on PA frequency. The MPAS-A present-climate simulations reproduce the main centers of observed PA activity, but with an eastward shift in the North Pacific and reduced amplitude in the North Atlantic. The overall frequency of positive PAs in the future simulations is similar to that in the present-day simulations, while negative PAs become less frequent. Although some regional changes emerge, the small, generally negative changes in PA frequency and meridional circulation index indicate that climate change does not lead to increased persistence of midlatitude flow anomalies or increased waviness in these simulations.

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Jing-Yi Zhuo and Zhe-Min Tan

Abstract

A deep learning–based method augmented by prior knowledge of tropical cyclones (TCs), called DeepTCNet, is introduced to estimate TC intensity and wind radii from infrared (IR) imagery over the North Atlantic Ocean. While standard deep learning practices have many advantages over conventional analysis approaches and can produce reliable estimates of TCs, the data-driven models informed by machine-readable physical knowledge of TCs could achieve higher performance. To this end, two approaches are explored to develop the physics-augmented DeepTCNet: (i) infusing the auxiliary physical information of TCs into models for single-task learning and (ii) learning auxiliary physical tasks for multitask learning. More specifically, augmented by auxiliary information of TC fullness (a measure of the radial decay of the TC wind field), the DeepTCNet yields a 12% improvement in estimating TC intensity over the nonaugmented one. By learning TC wind radii and auxiliary TC intensity task simultaneously, the model’s wind radii estimation skill is improved by 6% over only learning four wind radii tasks and by 9% over separately learning a single wind radii task. The evaluation results showed that the DeepTCNet is in-line with the Satellite Consensus technique (SATCON) but systematically outperforms the advanced Dvorak technique (ADT) at all intensity scales with an averaged 39% enhancement in TC intensity estimation. The DeepTCNet also surpasses the Multiplatform Tropical Cyclone Surface Wind Analysis technique (MTCSWA) with an average improvement of 32% in wind radii estimation.

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Milind Sharma, Robin L. Tanamachi, Eric C. Bruning, and Kristin M. Calhoun

SIGNIFICANCE STATEMENT:

Lightning is a manifestation of collisions between hydrometeor species, especially ice crystals and graupel. There still exist gaps in our understanding of the physical processes that link macroscopic properties of storms and their electrification characteristics. This study exploits dual-polarization radar signatures to characterize the temporal variability in the microphysical and electrical properties of a tornadic supercell. This particular storm maintained an inverted polarity charge structure throughout its mature phase. Pulses in the storm’s updraft (inferred from behavior of differential reflectivity columns) were associated with jumps in lightning flash rates. Finally, we show that the time variations in lightning activity can be explained by changes in differential reflectivity column volume and height.

Open access
Inez Z. Ponce de Leon

Abstract

Supertyphoon Haiyan hit the Philippines in 2013, causing massive damage and loss of lives. The media blamed the government for faulty warnings, including using the term “storm surge,” which people reportedly did not understand. As a result, the national agency tasked with disaster risk management recommended translating the term for better response in future storms. Such an approach shortchanges the complexity of risk construction and dismisses the possibility that different communities also have different understandings of risk. In this study, the researcher examined the special case of Coron, Palawan: a major tourist destination that is rarely hit by storms but that became the site of Haiyan’s last landfall. Guided by encoding–decoding theory, the researcher interviewed local government officials and carried out focus group discussions with representatives of two communities (whose names have been hidden under pseudonyms for this study): Central, close to the municipal center, and Island, a coastal village far away from potential aid and rescue. The researcher found a portrait of contrasts that split Coron: a mayor who surrendered all control and a risk management officer who planned for long-term hazard response—Island waiting for government instructions despite knowing about storm behavior and Central taking the initiative to create long-term solutions. Island also knew what storm surges were and did not need translation of the term. These findings show that risk constructions can differ even at the municipal level, which should prompt further research into the role of local knowledge in understanding risk and hazard warnings.

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Robert G. Nystrom, Steven J. Greybush, Xingchao Chen, and Fuqing Zhang

Abstract

The tropical cyclone (TC) surface-exchange coefficients of enthalpy (C k) and momentum (C d) at high wind speeds have been notoriously challenging to estimate. This difficulty arises from many factors, including the difficulties in collecting observations within the turbulent TC boundary layer, and the complex coupled physical interactions between the TC boundary layer and ocean surface, which are challenging to accurately model. Motivated by recent studies highlighting the limited practical predictability of TC intensity as a result of uncertainty in the physical representation of the air–sea fluxes of momentum and enthalpy at high wind speeds, we investigate the potential to estimate the surface enthalpy and momentum exchange coefficients through ensemble data assimilation. Significant ensemble correlations between tangential wind, radial wind, and simulated infrared brightness temperatures with parameters controlling the enthalpy and momentum exchange coefficients suggest potential to use all-sky satellite and/or airborne radial velocity observations to estimate these unknown parameters. Using a series of observing system simulation experiments (OSSEs), simulated infrared brightness temperature observations, and a known truth, we demonstrate some potential for simultaneous state and parameter estimation with an ensemble-based data assimilation system to converge toward the correct known parameter values. In all OSSEs with either one or multiple unknown parameters, the initial parameter errors are reduced through simultaneous model state and parameter estimation. However, challenges still exist in converging to the precise true parameter values, as state errors during rapid intensification can project onto the parameter estimates.

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Wenhao Dong, Ming Zhao, Yi Ming, and V. Ramaswamy

Abstract

The characteristics of tropical mesoscale convective systems (MCSs) simulated with a finer-resolution (~50 km) version of the Geophysical Fluid Dynamics Laboratory (GFDL) AM4 model are evaluated by comparing with a comprehensive long-term observational dataset. It is shown that the model can capture the various aspects of MCSs reasonably well. The simulated spatial distribution of MCSs is broadly in agreement with the observations. This is also true for seasonality and interannual variability over different land and oceanic regions. The simulated MCSs are generally longer-lived, weaker, and larger than observed. Despite these biases, an event-scale analysis suggests that their duration, intensity, and size are strongly correlated. Specifically, longer-lived and stronger events tend to be bigger, which is consistent with the observations. The same model is used to investigate the response of tropical MCSs to global warming using time-slice simulations forced by prescribed sea surface temperatures and sea ice. There is an overall decrease in occurrence frequency, and the reduction over land is more prominent than over ocean.

Open access
Jae-Heung Park, Mi-Kyung Sung, Young-Min Yang, Jiuwei Zhao, Soon-Il An, and Jong-Seong Kug

Abstract

The North Pacific Oscillation (NPO), a primary atmospheric mode over the North Pacific Ocean in boreal winter, is known to trigger El Niño–Southern Oscillation (ENSO) in the following winter, the process of which is recognized as the seasonal footprinting mechanism (SFM). On the basis of the analysis of model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5), we found that the SFM acts differently among models, and the correlation between the NPO and subsequent ENSO events, called the SFM efficiency, depends on the background mean state of the model. That is, SFM efficiency becomes stronger as the climatological position of the Pacific intertropical convergence zone (ITCZ) moves poleward, representing an intensification of the northern branch of the ITCZ. When the Pacific ITCZ is located poleward, the wind–evaporation–sea surface temperature (SST) feedback becomes stronger as the precipitation response to the SST anomaly is stronger in higher latitudes than that in lower latitudes. In addition, such active ocean–atmosphere interactions enhance NPO variability, favoring the SFM to operate efficiently and trigger an ENSO event. Consistent with the model results, the observed SFM efficiency increased during the decades in which the northern branch of the climatological ITCZ was intensified, supporting the importance of the tropical mean state of precipitation around the Pacific ITCZ.

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Chunxue Yang, Francesca Elisa Leonelli, Salvatore Marullo, Vincenzo Artale, Helen Beggs, Bruno Buongiorno Nardelli, Toshio M. Chin, Vincenzo De Toma, Simon Good, Boyin Huang, Christopher J. Merchant, Toshiyuki Sakurai, Rosalia Santoleri, Jorge Vazquez-Cuervo, Huai-Min Zhang, and Andrea Pisano

Abstract

A joint effort between the Copernicus Climate Change Service (C3S) and the Group for High Resolution Sea Surface Temperature (GHRSST) has been dedicated to an intercomparison study of eight global gap-free sea surface temperature (SST) products to assess their accurate representation of the SST relevant to climate analysis. In general, all SST products show consistent spatial patterns and temporal variability during the overlapping time period (2003–18). The main differences between each product are located in the western boundary current and Antarctic Circumpolar Current regions. Linear trends display consistent SST spatial patterns among all products and exhibit a strong warming trend from 2012 to 2018 with the Pacific Ocean basin as the main contributor. The SST discrepancy between all SST products is very small compared to the significant warming trend. Spatial power spectral density shows that the interpolation into 1° spatial resolution has negligible impacts on our results. The global mean SST time series reveals larger differences among all SST products during the early period of the satellite era (1982–2002) when there were fewer observations, indicating that the observation frequency is the main constraint of the SST climatology. The maturity matrix scores, which present the maturity of each product in terms of documentation, storage, and dissemination but not the scientific quality, demonstrate that ESA-CCI and OSTIA SST are well documented for users’ convenience. Improvements could be made for MGDSST and BoM SST. Finally, we have recommended that these SST products can be used for fundamental climate applications and climate studies (e.g., El Niño).

Open access
Soong-Ki Kim and Soon-Il An

Abstract

The life cycle of El Niño–Southern Oscillation (ENSO) typically follows a seasonal march, with onset in spring, developing during summer, maturing in boreal winter, and decaying over the following spring. This feature is referred to as ENSO phase locking. Recent studies have noted that seasonal modulation of the ENSO growth rate is essential for this process. This study investigates the fundamental effect of a seasonally varying growth rate on ENSO phase locking using a modified seasonally dependent recharge oscillator model. There are two phase locking regimes associated with the strength of the seasonal modulation of growth rate: 1) a weak regime in which only a single peak occurs and 2) a strong regime in which two types of events occur either with a single peak or with a double peak. Notably, there is a seasonal gap in the strong regime, during which the ENSO peak cannot occur because of large-scale ocean–atmosphere coupled processes. We also retrieve a simple analytical solution of the seasonal variance of ENSO, revealing that the variance is governed by the time integral of seasonally varying growth rate. Based on this formulation, we propose a seasonal energy index (SEI) that explains the seasonal gap and provides an intuitive explanation for ENSO phase locking, potentially applicable to global climate model ENSO diagnostics.

Open access