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Amit Bhardwaj, Vasubandhu Misra, Ben Kirtman, Tirusew Asefa, Carolina Maran, Kevin Morris, Ed Carter, Christopher Martinez, and Daniel Roberts

Abstract

We present here the analysis of 20 years of high-resolution experimental winter seasonal climate reforecasts for Florida (CLIFF). These winter seasonal reforecasts were dynamically downscaled by a regional atmospheric model at 10-km grid spacing from a global model run at T62 spectral resolution (~210-km grid spacing at the equator) forced with sea surface temperatures (SST) obtained from one of the global models in the North American Multimodel Ensemble (NMME). CLIFF was designed in consultation with water managers (in utilities and public water supply) in Florida targeting its five water management districts, including two smaller watersheds of two specific stakeholders in central Florida that manage the public water supply. This enterprise was undertaken in an attempt to meet the climate forecast needs of water management in Florida. CLIFF has 30 ensemble members per season generated by changes to the physics and the lateral boundary conditions of the regional atmospheric model. Both deterministic and probabilistic skill measures of the seasonal precipitation at the zero-month lead for November–December–January (NDJ) and one-month lead for December–January–February (DJF) show that CLIFF has higher seasonal prediction skill than persistence. The results of the seasonal prediction skill of land surface temperature are more sobering than precipitation, although, in many instances, it is still better than the persistence skill.

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Aaron J. Hill, Christopher C. Weiss, and David C. Dowell

Abstract

Ensemble forecasts are generated with and without the assimilation of near-surface observations from a portable, mesoscale network of StickNet platforms during the Verification of the Origins of Rotation in Tornadoes Experiment–Southeast (VORTEX-SE). Four VORTEX-SE intensive observing periods are selected to evaluate the impact of StickNet observations on forecasts and predictability of deep convection within the Southeast United States. StickNet observations are assimilated with an experimental version of the High-Resolution Rapid Refresh Ensemble (HRRRE) in one experiment, and withheld in a control forecast experiment. Overall, StickNet observations are found to effectively reduce mesoscale analysis and forecast errors of temperature and dewpoint. Differences in ensemble analyses between the two parallel experiments are maximized near the StickNet array and then either propagate away with the mean low-level flow through the forecast period or remain quasi-stationary, reducing local analysis biases. Forecast errors of temperature and dewpoint exhibit periods of improvement and degradation relative to the control forecast, and error increases are largely driven on the storm scale. Convection predictability, measured through subjective evaluation and objective verification of forecast updraft helicity, is driven more by when forecasts are initialized (i.e., more data assimilation cycles with conventional observations) rather than the inclusion of StickNet observations in data assimilation. It is hypothesized that the full impact of assimilating these data is not realized in part due to poor sampling of forecast sensitive regions by the StickNet platforms, as identified through ensemble sensitivity analysis.

Open access
Torbjørn Selseng, Marit Klemetsen, and Tone Rusdal

Abstract

In recent decades there has been a surge in the scholarship on climate change adaptation (CCA) terminology, and diverging interpretations of the term have emerged. Given the crucial role of local governments in building societywide adaptive capacity, understanding how municipalities understand and interpret CCA is important. In this study, we analyze 12 large-scale questionnaires from 2007 to 2020 distributed to all Norwegian municipalities. Using a combination of directed and conventional content analysis of the questions and answers, we summarize and map the progress of adaptation work over the 14 years and assess the consistency and the scope of the surveys in light of the current research on climate adaptation. We find diverging views on what adaptation entails, both from the researchers, in the phrasing of questions, and from the respondents. The empirical evidence suggests an overall imbalanced interpretation of CCA, in terms of the risks and consequences we may face, the climate to which adapting is needed, and adequate adaptation strategies. We go on to discuss the implications of these findings, highlighting the need for a shared and well-communicated framework for local CCA and a closer monitoring of the actual efforts of the municipalities. If instead left unchecked, this confusion might lead to unsustainable maladaptation at the local government level throughout Norway and beyond.

Open access
F. Joseph Turk, Sarah E. Ringerud, Yalei You, Andrea Camplani, Daniele Casella, Giulia Panegrossi, Paolo Sanò, Ardeshir Ebtehaj, Clement Guilloteau, Nobuyuki Utsumi, Catherine Prigent, and Christa Peters-Lidard

Abstract

A fully global satellite-based precipitation estimate that can transition across the changing Earth surface and complex land/water conditions is an important capability for many hydrological applications, and for independent evaluation of the precipitation derived from weather and climate models. This capability is inherently challenging owing to the complexity of the surface geophysical properties upon which the satellite-based instruments view. To date, these satellite observations originate primarily from a variety of wide-swath passive microwave (MW) imagers and sounders. In contrast to open ocean and large water bodies, the surface emissivity contribution to passive MW measurements is much more variable for land surfaces, with varying sensitivities to near-surface precipitation. The NASA–JAXA Global Precipitation Measurement (GPM) spacecraft (2014–present) is equipped with a dual-frequency precipitation radar and a multichannel passive MW imaging radiometer specifically designed for precipitation measurement, covering substantially more land area than its predecessor Tropical Rainfall Measuring Mission (TRMM). The synergy between GPM’s instruments has guided a number of new frameworks for passive MW precipitation retrieval algorithms, whereby the information carried by the single narrow-swath precipitation radar is exploited to recover precipitation from a disparate constellation of passive MW imagers and sounders. With over 6 years of increased land surface coverage provided by GPM, new insight has been gained into the nature of the microwave surface emissivity over land and ice/snow-covered surfaces, leading to improvements in a number of physically and semiphysically based precipitation retrieval techniques that adapt to variable Earth surface conditions. In this manuscript, the workings and capabilities of several of these approaches are highlighted.

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Hui Li, Alexey Fedorov, and Wei Liu

Abstract

This study compares the impacts of Arctic sea ice decline on the Atlantic meridional overturning circulation (AMOC) in two configurations of the Community Earth System Model with different horizontal resolution. In a suite of model experiments, we impose radiative imbalance at the ice surface, replicating a loss of sea ice cover comparable to that observed during 1979–2014, and we find dramatic differences in the AMOC response between the two models. In the lower-resolution configuration, the AMOC weakens by about one-third over the first 100 years, approaching a new quasi-equilibrium. By contrast, in the higher-resolution configuration, the AMOC weakens by ~10% during the first 20–30 years followed by a full recovery driven by invigorated deep water formation in the Labrador Sea and adjacent regions. We investigate these differences using a diagnostic AMOC stability indicator, which reflects the AMOC freshwater transport in and out of the basin and hence the strength of the basin-scale salt-advection feedback. This indicator suggests that the AMOC in the lower-resolution model is less stable and more sensitive to surface perturbations, as confirmed by hosing experiments mimicking Arctic freshening due to sea ice decline. Differences between the models’ mean states, including the Atlantic Ocean mean surface freshwater fluxes, control the differences in AMOC stability. Our results demonstrate that the AMOC stability indicator is indeed useful for evaluating AMOC sensitivity to perturbations. We emphasize that, despite the differences in the long-term adjustment, both models simulate a multidecadal AMOC weakening caused by Arctic sea ice decline, relevant to climate change.

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Aoyun Xue, Wenjun Zhang, Julien Boucharel, and Fei-Fei Jin

Abstract

Although the 1997/98 and 2015/16 El Niño events are considered to be the strongest on record, their subsequent La Niña events exhibited contrasted evolutions. In this study, we demonstrate that the extremely strong period of tropical instability waves (TIWs) at the beginning of boreal summer of 2016 played an important role in hindering the subsequent La Niña’s development by transporting extra off-equatorial heat into the Pacific cold tongue. By comparing the TIWs’ contribution based on an oceanic mixed layer heat budget analysis for the 1998 and 2016 episodes, we establish that TIW-induced nonlinear dynamical heating (NDH) is a significant contributor to the El Niño–Southern Oscillation (ENSO) phase transition in 2016. TIW-induced NDH contributed to around 0.4°C warming per month during the early boreal summer (May–June) following the 2015/16 El Niño’s peak, which is found to be an essential inhibiting factor that prevented the subsequent La Niña’s growth. A time-mean eddy kinetic energy analysis reveals that anomalous TIWs during 2016 mainly gained their energy from the baroclinic instability conversion due to a strong SST warming in the northeastern off-equatorial Pacific that promoted an increased meridional SST gradient. This highlights the importance of accurately reproducing TIW activity in ENSO simulation and the benefit of off-equatorial SST anomalies in the eastern Pacific as an independent precursor for ENSO predictions.

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Philippe Goulet Coulombe and Maximilian Göbel

Abstract

On 15 September 2020, Arctic sea ice extent (SIE) ranked second-to-lowest in history and keeps trending downward. The understanding of how feedback loops amplify the effects of external CO2 forcing is still limited. We propose the VARCTIC model, which is a vector autoregression (VAR) designed to capture and extrapolate Arctic feedback loops. VARs are dynamic simultaneous systems of equations, routinely estimated to predict and understand the interactions of multiple macroeconomic time series. The VARCTIC is a parsimonious compromise between full-blown climate models and purely statistical approaches that usually offer little explanation of the underlying mechanism. Our completely unconditional forecast has SIE hitting 0 in September by the 2060s. Impulse response functions reveal that anthropogenic CO2 emission shocks have an unusually durable effect on SIE—a property shared by no other shock. We find albedo- and thickness-based feedbacks to be the main amplification channels through which CO2 anomalies impact SIE in the short and medium runs. Furthermore, conditional forecast analyses reveal that the future path of SIE crucially depends on the evolution of CO2 emissions, with outcomes ranging from recovering SIE to it reaching 0 in the 2050s. Finally, albedo and thickness feedbacks are shown to play an important role in accelerating the speed at which predicted SIE is heading toward 0.

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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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Amélie Desmarais and L. Bruno Tremblay

Abstract

Uncertainties in the timing of a seasonal ice cover in the Arctic Ocean depend on model physics and parameterizations, natural variability at decadal time scales, and uncertainties in climate scenarios and forcings. We use the Gridded Monthly Sea Ice Extent and Concentration, 1850 Onward data product to assess the simulated decadal variability from the Community Earth System Model–Large Ensemble (CESM-LE) in the Pacific, Eurasian, and Atlantic sectors of the Arctic where a longer observational record exists. Results show that sea ice decadal (8–16 years) variability in CESM-LE is in agreement with the observational record in the Pacific sector of the Arctic, underestimated in the Eurasian sector of the Arctic, specifically in the East Siberian Sea, and slightly overestimated in the Atlantic sector of the Arctic, specifically in the Greenland Sea. Results also show an increase in variability at decadal time scales in the Eurasian and Pacific sectors during the transition to a seasonally ice-free Arctic, in agreement with the observational record although this increase is delayed by 10–20 years. If the current sea ice retreat in the Arctic continues to be Pacific-centric, results from the CESM-LE suggest that uncertainty in the timing of an ice-free Arctic associated with natural variability is realistic, but that a seasonal ice cover may occur earlier than projected.

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Guo-Yuan Lien, Chung-Han Lin, Zih-Mao Huang, Wen-Hsin Teng, Jen-Her Chen, Ching-Chieh Lin, Hsu-Hui Ho, Jyun-Ying Huang, Jing-Shan Hong, Chia-Ping Cheng, and Ching-Yuang Huang

Abstract

The FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) Radio Occultation (RO) satellite constellation was launched in June 2019 as a successor of the FORMOSAT-3/COSMIC mission. The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from the Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semioperational experiment assimilating the FORMOSAT-7/COSMIC-2 bending angle data with all other operational observation data has been conducted to evaluate the impact of the FORMOSAT-7/COSMIC-2 data. The first seven-month results show that the quality of the early FORMOSAT-7/COSMIC-2 data has been satisfactory for assimilation. Consistent and significant positive impacts on global forecast skills have been observed since the start of the parallel experiment, with the most significant impact found in the tropical region, reflecting the low-inclination orbital design of the satellites. The impact of the FORMOSAT-7/COSMIC-2 RO data is also estimated using the ensemble forecast sensitivity to observation impact (EFSOI) method, showing an average positive impact per observation similar to other existing GNSS RO datasets, while the total impact is impressive by virtue of its large amount. Sensitivity experiments suggest that the quality control processes built in the Gridpoint Statistical Interpolation (GSI) system for RO data work well to achieve a positive impact by the low-level FORMOSAT-7/COSMIC-2 RO data, while more effort on observation error tuning should be focused to obtain an optimal assimilation performance. This study demonstrates the usefulness of the FORMOSAT-7/COSMIC-2 RO data in global numerical weather prediction during the calibration/validation period and leads to the operational use of the data at CWB.

Open access