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Liqiao Liang, Lan Cuo, and Qiang Liu

Abstract

Understanding the effects of the snow ratio on glacier mass balance under variable climatic conditions is crucial for predicting how glaciers will respond to climate change, and for forecasting water supplies to surrounding lowland areas. Due to recent climate change, the historical annual snow ratio of the Dongkemadi (DKMD) Glacier showed a significant increasing trend (0.0538% a−1, p < 0.05), and an abrupt upward change in 1977 due to decreasing precipitation concentration. Snow ratios with fixed precipitation concentration and nonwarming climate scenarios were calculated to isolate the impact of the snow ratio on glacier mass balance. Under nonwarming conditions, the snow ratio showed little variability, ranging from 88.4% to 99.9%. Glacier modeling results comparing five snow ratio scenarios from 1961 to 2009 showed three main features as follows. (i) Glacier mass balance was low and more sensitive to a warming climate for lower snow ratio scenarios. (ii) The difference in mass balance between the scenarios fluctuated, but generally increased with time. Spatially, the ablation area change was larger (0.4 km2), and the equilibrium line altitude was higher (5.9 m) in scenarios with lower snow ratios. (iii) The change in net shortwave radiation was the main reason for changes in glacial melt, and the albedo played a key role in controlling the difference of glacier energy balance between snow ratio scenarios. Rain increment only accounted for about 20%–33% of meltwater increment. Overall, this study provides valuable information to evaluate how snow ratios impact the mass balance of glaciers with ongoing climate change.

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Shuangwen Sun, Yue Fang, Yongcan Zu, and Baochao Liu

Abstract

The thermocline depth in the tropical Indian Ocean has experienced dramatic decadal variations in recent decades. Using analysis and reanalysis datasets, we find that the decadal thermocline depth anomalies show large seasonal differences. The seasonal differences are modulated by two major modes. The first mode shows a zonal dipole pattern, with opposite thermocline depth anomalies in the equatorial eastern Indian Ocean and western Indian Ocean, and is prominent in summer and winter. The second mode is characterized by marked thermocline depth anomalies in the southern Indian Ocean and is significant in spring and fall. The amplitude of the seasonal oscillation in these two modes has increased substantially in the twenty-first century. Their phase change is in good agreement with the observed thermocline depth anomalies in each season. The results also show that the seasonality of the decadal thermocline depth anomalies arises directly from surface wind variations within the Indian Ocean. The first mode is mainly caused by equatorial zonal wind anomalies. The second mode is dominated by local wind stress curl anomalies. These wind anomalies are both significantly correlated with the ENSO-like SST anomalies in the Pacific Ocean. The findings improve our understanding of the decadal thermocline anomalies, and will help to better evaluate their impact on seasonal phase-locked oceanic and atmospheric processes.

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Cheng Zheng, Mingfang Ting, Yutian Wu, Nathan Kurtz, Clara Orbe, Patrick Alexander, Richard Seager, and Marco Tedesco

Abstract

We investigate wintertime extreme sea ice loss events on synoptic to subseasonal time scales over the Barents–Kara Sea, where the largest sea ice variability is located. Consistent with previous studies, extreme sea ice loss events are associated with moisture intrusions over the Barents–Kara Sea, which are driven by the large-scale atmospheric circulation. In addition to the role of downward longwave radiation associated with moisture intrusions, which is emphasized by previous studies, our analysis shows that strong turbulent heat fluxes are associated with extreme sea ice melting events, with both turbulent sensible and latent heat fluxes contributing, although turbulent sensible heat fluxes dominate. Our analysis also shows that these events are connected to tropical convective anomalies. A dipole pattern of convective anomalies with enhanced convection over the Maritime Continent and suppressed convection over the central to eastern Pacific is consistently detected about 6–10 days prior to extreme sea ice loss events. This pattern is associated with either the Madden–Julian oscillation (MJO) or El Niño–Southern Oscillation (ENSO). Composites show that extreme sea ice loss events are connected to tropical convection via Rossby wave propagation in the midlatitudes. However, tropical convective anomalies alone are not sufficient to trigger extreme sea ice loss events, suggesting that extratropical variability likely modulates the connection between tropical convection and extreme sea ice loss events.

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Elliott M. Sainsbury, Reinhard K. H. Schiemann, Kevin I. Hodges, Alexander J. Baker, Len C. Shaffrey, and Kieran T. Bhatia

Abstract

Recurving tropical cyclones (TCs) can cause extensive damage along the U.S. East Coast and later in their life cycle over Europe as post-tropical cyclones. While the existing literature attempts to understand the drivers of basinwide and regional TC variability, less work has been undertaken looking at recurving TCs. The roles played by the interannual variabilities of TC frequency and the steering flow in governing recurving TC interannual variability are investigated in this study. Using a track-matching algorithm, we identify observed TC tracks from the NHC “best track” hurricane database, version 2 (HURDAT2) in the ERA5 and MERRA2 reanalyses. This allows for detailed analysis of the post-tropical stages of the tracks in the observational TC record, enabling robust identification and separation of TCs that recurve. We show that over 75% of the interannual variance in annual recurving TC frequency can be explained by just two predictors—the frequency of TCs forming in the subtropical Atlantic, and hurricanes (TCs with wind speeds > 33 m s−1) forming in the main development region (MDR). An index describing the seasonal mean meridional steering flow shows a weak, nonsignificant relationship with recurving TC frequency, supported by composite analysis. These results show that the interannual variability in recurving TC frequency is primarily driven by the seasonal TC activity of the MDR and the subtropical Atlantic, with seasonal anomalies in the steering flow playing a much smaller, secondary role. These results help to quantify the extent to which skillful seasonal forecasts of Atlantic hurricane activity benefit regions vulnerable to recurving TCs.

Significance Statement

Recurving tropical cyclones (TCs) can cause extensive damage to the U.S. East Coast, eastern Canada, and Europe. It is, therefore, crucial to understand why some years have a higher frequency of recurving TCs than other years. In this study, we show that the frequency of recurving TCs is very strongly linked to the frequency that hurricanes (TCs with wind speeds > 33 m s−1) form in the main development region, and the frequency that TCs form in the subtropical Atlantic. This result suggests that skillful seasonal prediction of hurricane activity could be used to give enhanced seasonal warning to the regions often impacted by recurving TCs.

Open access
Andrzej Z. Kotarba and Z˙aneta Nguyen Huu

Abstract

The longest cirrus time series are ground-based, visual observations captured by human observers [synoptic observations (SYNOP)]. However, their reliability is impacted by an unfavorable viewing geometry (cloud overlap) and misclassification due to low cloud optical thickness, especially at night. For the very first time, this study assigns a quantitative value to uncertainty. We validate 15 years of SYNOP observations (2006–20) against data from the cloud lidar flown on board the Cloud–Aerosol Lidar and Infrared Pathfinder (CALIPSO) spacecraft. We develop a dedicated method to match SYNOP reports (with a hemispherical field of view) with lidar samples (along-track profiles). Our evaluation of the human eye’s sensitivity to cirrus revealed that it is moderate, at best. In perfect conditions (daytime with no mid/low-level clouds) the probability of correct detection was 44%–83% (Cohen’s kappa coefficient < 0.6), and this fell to 24%–42% (kappa < 0.3) at night. Lunar illumination improved detection, but only when the moon’s phase exceeded 50%. Cirrus optical depth had a clear impact on detection. When clouds at all levels were considered (i.e., real-life conditions), the reliability of the visual method was moderate to poor: it detected 47%–71% of cirrus (kappa < 0.45) during the day and 28%–43% (kappa < 0.2) at night and decreased with an increasing low/midlevel cloud fraction. These kappa coefficients suggest that agreement with CALIPSO data was close to random. Our findings can be directly applied to estimations of cirrus frequency/trends. Our reported probabilities of detection can serve as a benchmark for other ground-based cirrus detection methods.

Significance Statement

Cirrus clouds heat the atmosphere, so any increase in their frequency will contribute to climate warming. The longest cirrus time series (including the presatellite era) are surface-based detections by a human observer at a meteorological station. Our study is the first to quantitatively evaluate the reliability of these observations. Our results show that, because of the viewing geometry (cloud overlap) and human eye sensitivity, reliability ranges from moderate at best to very low. Nighttime detections are especially unreliable, as well as those in the presence of low/midlevel cloud. Cirrus frequencies and trends calculated from visual observations should, thus, be considered with caution.

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Katie Kirk, Gregory Dusek, Philippe Tissot, and William Sweet

Abstract

The demand for nearshore wave observations is increasing due to spatial gaps and the importance of observations for accurate models and better understanding of inundation processes. Here, we show how water level (WL) standard deviation (sigma, σ) measurements at three acoustic NOAA tide gauges that utilize an Aquatrak sensor [Duck, North Carolina, Bob Hall Pier (BHP) in Corpus Christi, Texas, and Lake Worth, Florida] can be used as a proxy for significant wave height (H m0). Sigma-derived H m0 is calibrated to best fit nearby wave observations and error is quantified through RMSE, normalized RMSE (NRMSE), bias, and a scatter index. At Duck and Lake Worth, a quadratic fit of sigma to nearby wave observations results in a R 2 of 0.97 and 0.83, RMSE of 0.11 and 0.11 m, and NRMSE of 0.09 and 0.22, respectively. A linear fit between BHP sigma and H m0 is best, resulting in R 2 0.62, RMSE of 0.22, and NRMSE of 0.26. Regression fits deviate across NOAA stations and from the classic relationship of H m0 = 4σ, indicating H m0 cannot be accurately estimated with this approach at these Aquatrak sites. The dynamic water level (DWL = still WL ± 2σ) is calculated over the historic time series showing climatological and seasonal trends in the stations’ daily maximums. The historical DWL and sigma wave proxy could be calculated for many NOAA tide gauges dating back to 1996. These historical wave observations can be used to fill observational spatial gaps, validate models, and improve understanding of wave climates.

Significance Statement

There is a large spatial gap in nearshore real-time observational wave data that can provide critical information to researchers and resource managers regarding inundation and erosion, help validate coastal hydrodynamic models, and provide the maritime community with products that help ensure navigational safety. This study utilizes existing infrastructure to help fill the demand for nearshore wave observations by deriving a proxy for wave height at three sites. This work shows spatial variability in the regression fits across the sites, which should be explored at more stations in future work. Multidecadal length time series were also used at the sites to investigate climatological and seasonal trends that provide insight into wave climates and wave driven processes important for coastal flooding.

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Xing Luo, Jun Ge, Weidong Guo, Lei Fan, Chaorong Chen, Yu Liu, and Limei Yang

Abstract

Deforestation can impact precipitation through biophysical processes and such effects are commonly examined by models. However, previous studies mostly conduct deforestation experiments with a single model and the simulated precipitation responses to deforestation diverge across studies. In this study, 11 Earth system models are used to robustly examine the biophysical impacts of deforestation on precipitation, precipitation extremes, and the seasonal pattern of the rainy season through a comparison of a control simulation and an idealized global deforestation simulation with clearings of 20 million km2 of forests. The multimodel mean suggests decreased precipitation, reduced frequency and intensity of heavy precipitation, and shortened duration of rainy seasons over deforested areas. The deforestation effects can even propagate to some regions that are remote from deforested areas (e.g., the tropical and subtropical oceans and the Arctic Ocean). Nevertheless, the 11 models do not fully agree on the precipitation changes almost everywhere. In general, the models exhibit higher consistency over the deforested areas and a few regions outside the deforested areas (e.g., the subtropical oceans) but lower consistency over other regions. Such intermodel spread mostly results from divergent responses of evapotranspiration and atmospheric moisture convergence to deforestation across the models. One of the models that has multiple simulation members also reveals considerable spread of the precipitation responses to deforestation across the members due to internal model variability. This study highlights the necessity of robustly examining precipitation responses to deforestation based on multiple models and each model with multiple simulation members.

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Eric D. Loken, Adam J. Clark, and Amy McGovern

Abstract

Recent research has shown that random forests (RFs) can create skillful probabilistic severe weather hazard forecasts from numerical weather prediction (NWP) ensemble data. However, it remains unclear how RFs use NWP data and how predictors should be generated from NWP ensembles. This paper compares two methods for creating RFs for next-day severe weather prediction using simulated forecast data from the convection-allowing High-Resolution Ensemble Forecast System, version 2.1 (HREFv2.1). The first method uses predictors from individual ensemble members (IM) at the point of prediction, while the second uses ensemble mean (EM) predictors at multiple spatial points. IM and EM RFs are trained with all predictors as well as predictor subsets, and the Python module tree interpreter (TI) is used to assess RF variable importance and the relationships learned by the RFs. Results show that EM RFs have better objective skill compared to similarly configured IM RFs for all hazards, presumably because EM predictors contain less noise. In both IM and EM RFs, storm variables are found to be most important, followed by index and environment variables. Interestingly, RFs created from storm and index variables tend to produce forecasts with greater or equal skill than those from the all-predictor RFs. TI analysis shows that the RFs emphasize different predictors for different hazards in a way that makes physical sense. Further, TI shows that RFs create calibrated hazard probabilities based on complex, multivariate relationships that go well beyond thresholding 2–5-km updraft helicity.

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Sarah M. Larson, Yuko Okumura, Katinka Bellomo, and Melissa L. Breeden

Abstract

Identifying the origins of wintertime climate variations in the Northern Hemisphere requires careful attribution of the role of El Niño–Southern Oscillation (ENSO). For example, Aleutian low variability arises from internal atmospheric dynamics and is remotely forced mainly via ENSO. How ENSO modifies the local sea surface temperature (SST) and North American precipitation responses to Aleutian low variability remains unclear, as teasing out the ENSO signal is difficult. This study utilizes carefully designed coupled model experiments to address this issue. In the absence of ENSO, a deeper Aleutian low drives a positive Pacific decadal oscillation (PDO)-like SST response. However, unlike the observed PDO pattern, a coherent zonal band of turbulent heat flux–driven warm SST anomalies develops throughout the subtropical North Pacific. Furthermore, non-ENSO Aleutian low variability is associated with a large-scale atmospheric circulation pattern confined over the North Pacific and North America and dry precipitation anomalies across the southeastern United States. When ENSO is included in the forcing of Aleutian low variability in the experiments, the ENSO teleconnection modulates the turbulent heat fluxes and damps the subtropical SST anomalies induced by non-ENSO Aleutian low variability. Inclusion of ENSO forcing results in wet precipitation anomalies across the southeastern United States, unlike when the Aleutian low is driven by non-ENSO sources. Hence, we find that the ENSO teleconnection acts to destructively interfere with the subtropical North Pacific SST and southeastern United States precipitation signals associated with non-ENSO Aleutian low variability.

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Takaya Uchida, Bruno Deremble, and Stephane Popinet

Abstract

Mesoscale eddies, although being on scales of O(20–100) km, have a disproportionate role in shaping the mean stratification, which varies on the scale of O(1000) km. With the increase in computational power, we are now able to partially resolve the eddies in basin-scale and global ocean simulations, a model resolution often referred to as mesoscale permitting. It is well known, however, that due to gridscale numerical viscosity, mesoscale-permitting simulations have less energetic eddies and consequently weaker eddy feedback onto the mean flow. In this study, we run a quasigeostrophic model at mesoscale-resolving resolution in a double gyre configuration and formulate a deterministic closure for the eddy rectification term of potential vorticity (PV), namely, the eddy PV flux divergence. Our closure successfully reproduces the spatial patterns and magnitude of eddy kinetic and potential energy diagnosed from the mesoscale-resolving model. One novel point about our approach is that we account for nonlocal eddy feedbacks onto the mean flow by solving the “subgrid” eddy PV equation prognostically in addition to the mean PV.

Open access