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Maqsooda Mahomed, Alistair D. Clulow, Sheldon Strydom, Tafadzwanashe Mabhaudhi, and Michael J. Savage

Abstract

Climate change projections of increases in lightning activity are an added concern for lightning-prone countries such as South Africa. South Africa’s high levels of poverty, lack of education, and awareness, as well as a poorly developed infrastructure, increase the vulnerability of rural communities to the threat of lightning. Despite the existence of national lightning networks, lightning alerts and warnings are not disseminated well to such rural communities. We therefore developed a community-based early warning system (EWS) to detect and disseminate lightning threats and alerts in a timely and comprehensible manner within Swayimane, KwaZulu-Natal, South Africa. The system is composed of an electrical field meter and a lightning flash sensor with warnings disseminated via audible and visible alarms on site and with a remote server issuing short message services (SMSs) and email alerts. Twelve months of data (February 2018–February 2019) were utilized to evaluate the performance of the EWS’s detection and warning capabilities. Diurnal variations in lightning activity indicated the influence of solar radiation, causing convective conditions with peaks in lightning activity occurring during the late afternoon and early evening (between 1400 and 2100) coinciding with students being released from school and when most workers return home. In addition to detecting the threat of lightning, the EWS was beneficial in identifying periods that exhibited above-normal lightning activity, with two specific lightning events examined in detail. Poor network signals in rural communities presented an initial challenge, delaying data transmission to the central server until rectified using multiple network providers. Overall, the EWS was found to disseminate reliable warnings in a timely manner.

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Richard Seager, Naomi Henderson, Mark Cane, Honghai Zhang, and Jennifer Nakamura

Abstract

Persistent multiyear cold states of the tropical Pacific Ocean drive hydroclimate anomalies worldwide, including persistent droughts in the extratropical Americas. Here, the atmosphere and ocean dynamics and thermodynamics of multiyear cold states of the tropical Pacific Ocean are investigated using European Centre for Medium-Range Weather Forecasts reanalyses and simplified models of the ocean and atmosphere. The cold states are maintained by anomalous ocean heat flux divergence and damped by increased surface heat flux from the atmosphere to ocean. The anomalous ocean heat flux divergence is contributed to by both changes in the ocean circulation and thermal structure. The keys are an anomalously shallow thermocline that enhances cooling by upwelling and anomalous westward equatorial currents that enhance cold advection. The thermocline depth anomalies are shown to be a response to equatorial wind stress anomalies. The wind stress anomalies are shown to be a simple dynamical response to equatorial SST anomalies as mediated by precipitation anomalies. The cold states are fundamentally maintained by atmosphere–ocean coupling in the equatorial Pacific. The physical processes that maintain the cold states are well approximated by linear dynamics for ocean and atmosphere and simple thermodynamics.

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Alexander J. Ross, Ryan C. Grow, Lauren D. Hayhurst, Haley A. MacLeod, Graydon I. McKee, Kyle W. Stratton, Marissa E. Wegher, and Michael D. Rennie

Abstract

Groundhog Day is a widespread North American ritual that marks the onset of spring, with festivities centered around animals that humans believe have abilities to make seasonal predictions. Yet, the collective success of groundhog Marmota monax prognosticators has never been rigorously tested. Here, we propose the local climate-predicted phenology of early blooming spring plants (Carolina spring beauty, or Claytonia caroliniana, which overlaps in native range with groundhogs) as a novel and relevant descriptor of spring onset that can be applied comparatively across a broad geographical range. Of 530 unique groundhog-year predictions across 33 different locations, spring onset was correctly predicted by groundhogs exactly 50% of the time. While no singular groundhog predicted the timing of spring with any statistical significance, there were a handful of groundhogs with notable records of both successful and unsuccessful predictions: Essex Ed (Essex, Connecticut), Stonewall Jackson (Wantage, New Jersey), and Chuckles (Manchester, Connecticut) correctly predicted spring onset over 70% of the time. By contrast, Buckeye Chuck (Marion, Ohio), Dunkirk Dave (Dunkirk, New York), and Holland Huckleberry (Holland, Ohio) made incorrect predictions over 70% of the time. The two most widely recognized and long-tenured groundhogs in their respective countries—Wiarton Willie (Canada) and Punxsutawney Phil (United States)—had success rates of 54% and 52%, respectively, despite over 150 collective guesses. Using a novel phenological indicator of spring, this study determined, without a shadow of a doubt, that groundhog prognosticating abilities for the arrival of spring are no better than chance.

Open access
Terence J. O’Kane, Paul A. Sandery, Vassili Kitsios, Pavel Sakov, Matthew A. Chamberlain, Mark A. Collier, Russell Fiedler, Thomas S. Moore, Christopher C. Chapman, Bernadette M. Sloyan, and Richard J. Matear

Abstract

We detail the system design, model configuration, and data assimilation evaluation for the CSIRO Climate retrospective Analysis and Forecast Ensemble system, version 1 (CAFE60v1). CAFE60v1 has been designed with the intention of simultaneously generating both initial conditions for multiyear climate forecasts and a large ensemble retrospective analysis of the global climate system from 1960 to the present. Strongly coupled data assimilation (SCDA) is implemented via an ensemble transform Kalman filter in order to constrain a general circulation climate model to observations. Satellite (altimetry, sea surface temperature, sea ice concentration) and in situ ocean temperature and salinity profiles are directly assimilated each month, whereas atmospheric observations are subsampled from the JRA-55 atmospheric reanalysis. Strong coupling is implemented via explicit cross-domain covariances between ocean, atmosphere, sea ice, and ocean biogeochemistry. Atmospheric and surface ocean fields are available at daily resolution and monthly resolution for the land, subsurface ocean, and sea ice. The system produces 96 climate trajectories (state estimates) over the most recent six decades as well as a complete data archive of initial conditions, potentially enabling individual forecasts for all members each month over the 60-yr period. The size of the ensemble and application of strongly coupled data assimilation lead to new insights for future reanalyses.

Open access
Terence J. O’Kane, Paul A. Sandery, Vassili Kitsios, Pavel Sakov, Matthew A. Chamberlain, Dougal T. Squire, Mark A. Collier, Christopher C. Chapman, Russell Fiedler, Dylan Harries, Thomas S. Moore, Doug Richardson, James S. Risbey, Benjamin J. E. Schroeter, Serena Schroeter, Bernadette M. Sloyan, Carly Tozer, Ian G. Watterson, Amanda Black, Courtney Quinn, and Richard J. Matear

Abstract

The CSIRO Climate retrospective Analysis and Forecast Ensemble system, version 1 (CAFE60v1) provides a large (96 member) ensemble retrospective analysis of the global climate system from 1960 to present with sufficiently many realizations and at spatiotemporal resolutions suitable to enable probabilistic climate studies. Using a variant of the ensemble Kalman filter, 96 climate state estimates are generated over the most recent six decades. These state estimates are constrained by monthly mean ocean, atmosphere, and sea ice observations such that their trajectories track the observed state while enabling estimation of the uncertainties in the approximations to the retrospective mean climate over recent decades. For the atmosphere, we evaluate CAFE60v1 in comparison to empirical indices of the major climate teleconnections and blocking with various reanalysis products. Estimates of the large-scale ocean structure, transports, and biogeochemistry are compared to those derived from gridded observational products and climate model projections (CMIP). Sea ice (extent, concentration, and variability) and land surface (precipitation and surface air temperatures) are also compared to a variety of model and observational products. Our results show that CAFE60v1 is a useful, comprehensive, and unique data resource for studying internal climate variability and predictability, including the recent climate response to anthropogenic forcing on multiyear to decadal time scales.

Open access
K. Fagiewicz, P. Churski, T. Herodowicz, P. Kaczmarek, P. Lupa, J. Morawska-Jancelewicz, and A. Mizgajski

Abstract

This study determines the conditions and provides a recommendation for fostering cocreation for climate change adaptation and mitigation (CCA&M). In postulating that insufficient cocreation by stakeholders in the quadruple helix model is an important factor contributing to the low effectiveness of climate actions in the regions, we have focused our research on identifying real stakeholder engagement in climate action and identifying the needs, barriers, and drivers for strengthening the cocreation process. We identified the needs for action highlighted by stakeholders as having an impact on reducing barriers and stimulating drivers. We treated the identified needs for action as deep leverage points (intent and design) focused on three realms—knowledge, values, and institutions—in which engagement and cocreation can be strengthened and have the potential to increase the effectiveness of climate action taken by stakeholders within our quadruple helix. We recommend knowledge-based cocreation, which puts the importance of climate action in the value system and leads to paradigm reevaluation. The implementation of the identified needs for action requires the support of institutions, whereby they develop standards of cooperation and mechanisms for their implementation as a sustainable framework for stakeholder cooperation. The research has proved how the quadruple helix operates for climate action in the Poznań Agglomeration. We believe that this case study can be a reference point for regions at a similar level of development, and the methods used and results obtained can be applied in similar real contexts to foster local stakeholders in climate action.

Open access
Jun Ying, Tao Lian, Ping Huang, Gang Huang, Dake Chen, and Shangfeng Chen

Abstract

The surface heat flux anomalies during El Niño events have always been treated as an atmospheric response to sea surface temperature anomalies (SSTAs). However, whether they play roles in the formation of SSTAs remains unclear. In this study, we find that the surface net heat flux anomalies in different El Niño types have different effects on the development of the spatial pattern of SSTAs. By applying the fuzzy clustering method, El Niño events during 1982–2018 are classified into two types: 1) extreme El Niños with strong positive SSTAs, with the largest SSTAs in the eastern equatorial Pacific, and 2) moderate El Niños with moderate positive SSTAs, with the largest SSTAs in the central equatorial Pacific. The surface net heat flux anomalies in extreme El Niños generally display a “larger warming gets more damping” zonal paradigm, and essentially do not impact the formation of the spatial pattern of SSTAs. Those in moderate El Niños, however, can impact the formation of the spatial pattern of SSTAs by producing more damping effects in the eastern than in the central equatorial Pacific, thus favoring the largest SSTAs being confined to the central equatorial Pacific. More damping effects of net heat flux anomalies in the eastern equatorial Pacific in moderate El Niños are contributed by the surface latent heat flux anomalies, which are mainly regulated by the negative relative humidity–SST feedback and the positive wind–evaporation–SST feedback. Therefore, we highlight that these two atmospheric adjustments should be considered during the development of moderate El Niños in order to obtain a comprehensive understanding of the formation of El Niño diversity.

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Marcos Samuel Matias Ribeiro, Lara de Melo Barbosa Andrade, Maria Helena Constantino Spyrides, Kellen Carla Lima, Pollyane Evangelista da Silva, Douglas Toledo Batista, and Idemauro Antônio Rodrigues de Lara

Abstract

The occurrence of environmental disasters affects different social segments, impacting health, education, housing, economy, and the provision of basic services. Thus, the objective of this study was to estimate the relationship between the occurrence of disasters and extreme climate, sociosanitary, and demographic conditions in the Northeast region of Brazil (NEB) during the period from 1993 to 2013. Initially, we analyzed the spatial pattern of the incidence of events; subsequently, generalized additive models for location, scale, and shape were used to identify and estimate the magnitude of associations between factors. Results showed that droughts are the predominant disasters in NEB representing 81.1% of the cases, followed by events triggered by excessive rainfall such as flash floods (11.1%) and floods (7.8%). Climate conditions presented statistically significant associations with the analyzed disasters, in which indicators of excess rainfall positively contributed to the occurrence of flash floods and floods but negatively contributed to the occurrence of drought. Sociosanitary factors, such as percentage of households with inadequate sewage, waste collection, and water supply, were also positively associated with the model’s estimations, that is, contributing to an increase in the occurrence of events, with the exception of floods, which were not significantly influenced by sociosanitary parameters. A decrease of 19% in the risk of drought occurrence was estimated, on average. On the other hand, events caused by excessive rainfall increased by 40% and 57%, in the cases of flash floods and floods, respectively.

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Zoe Schroder and James B. Elsner

Abstract

Environmental variables are routinely used in estimating when and where tornadoes are likely to occur, but more work is needed to understand how tornado and casualty counts of severe weather outbreak vary with the larger-scale environmental factors. Here the authors demonstrate a method to quantify “outbreak”-level tornado and casualty counts with respect to variations in large-scale environmental factors. They do this by fitting negative binomial regression models to cluster-level environmental data to estimate the number of tornadoes and the number of casualties on days with at least 10 tornadoes. Results show that a 1000 J kg−1 increase in CAPE corresponds to a 5% increase in the number of tornadoes and a 28% increase in the number of casualties, conditional on at least 10 tornadoes and holding the other variables constant. Further, results show that a 10 m s−1 increase in deep-layer bulk shear corresponds to a 13% increase in tornadoes and a 98% increase in casualties, conditional on at least 10 tornadoes and holding the other variables constant. The casualty-count model quantifies the decline in the number of casualties per year and indicates that outbreaks have a larger impact in the Southeast than elsewhere after controlling for population and geographic area.

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Robert Ricker, Frank Kauker, Axel Schweiger, Stefan Hendricks, Jinlun Zhang, and Stephan Paul

Abstract

We investigate how sea ice decline in summer and warmer ocean and surface temperatures in winter affect sea ice growth in the Arctic. Sea ice volume changes are estimated from satellite observations during winter from 2002 to 2019 and are partitioned into thermodynamic growth and dynamic volume change. Both components are compared with validated sea ice–ocean models forced by reanalysis data to extend observations back to 1980 and to understand the mechanisms that cause the observed trends and variability. We find that a negative feedback driven by the increasing sea ice retreat in summer yields increasing thermodynamic ice growth during winter in the Arctic marginal seas eastward from the Laptev Sea to the Beaufort Sea. However, in the Barents and Kara Seas, this feedback seems to be overpowered by the impact of increasing oceanic heat flux and air temperatures, resulting in negative trends in thermodynamic ice growth of −2 km3 month−1 yr−1 on average over 2002–19 as derived from satellite observations.

Open access