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Northerly low-level jets (LLJ) along the eastern Andes are important conduits of moisture transport and play central roles in modulating precipitation in South America. This study further investigates the variability of the LLJ during extended austral summers. A new method characterizes the spatial extent of the LLJ and finds four distinct types: Central, Northern, Andes and Peru. We show the existence of specific evolutions such that the LLJ may initiate in the central region, expands along the Andes and terminates in the northern region. Conversely, the LLJ may propagate from north-to-south. The spatiotemporal evolution of the LLJ is remotely forced by Rossby wave trains propagating from the Pacific Ocean towards South America, and the different phases of the wave trains favor the occurrences of Central, Northern or Andes types. Occurrences of Central and Northern types are more frequent in El Niño and La Niña years, respectively. The persistence of precipitation is shown to be directly related to the persistence of the LLJ. Lastly, the Madden-Julian Oscillation plays an important role in generating wave trains modulating the frequency of LLJ, especially the Central type.

 

Abstract The rapid decline of summer Arctic sea ice over the past few decades has been driven by a combination of increasing greenhouse gases and internal variability of the climate system. However, uncertainties remain regarding spatial and temporal characteristics of the optimal internal atmospheric mode that most favors summer sea ice melting on low-frequency time scales. To pinpoint this mode, we conduct a suite of simulations in which atmospheric circulation is constrained by nudging tropospheric Arctic (60°–90°N) winds within the Community Earth System Model, version 1 (CESM1), to those from reanalysis. Each reanalysis year is repeated for over 10 model years using fixed greenhouse gas concentrations and the same initial conditions. Composites show the strongest September sea ice losses are closely preceded by a common June–August (JJA) barotropic anticyclonic circulation in the Arctic favoring shortwave absorption at the surface. Successive years of strong wind-driven melting also enhance declines in Arctic sea ice through enhancement of the ice–albedo feedback, reaching a quasi-equilibrium response after repeated wind forcing for over 5–6 years, as the effectiveness of the wind-driven ice–albedo feedback becomes saturated. Strong melting favored by a similar wind pattern as observations is detected in a long preindustrial simulation and 400-yr paleoclimate reanalysis, suggesting that a summer barotropic anticyclonic wind pattern represents the optimal internal atmospheric mode maximizing sea ice melting in both the model and natural world over a range of time scales. Considering strong contributions of this mode to changes in Arctic climate, a better understanding of its origin and maintenance is vital to improving future projections of Arctic sea ice. 

Abstract Over the past decades, Arctic climate has exhibited significant changes characterized by strong Pan-Arctic warming and a large scale wind shift trending toward an anticyclonic anomaly centered over Greenland and the Arctic ocean. Recent work has suggested that this wind change is able to warm the Arctic atmosphere and melt sea ice through dynamical-driven warming, moistening and ice drift effects. However, previous examination of this linkage lacks a capability to fully consider the complex nature of the sea ice response to the wind change. In this study, we perform a more rigorous test of this idea by using a coupled high-resolution modelling framework with observed winds nudged over the Arctic that allows for a comparison of these wind-induced effects with observations and simulated effects forced by anthropogenic forcing. Our nudging simulation can well capture observed variability of atmospheric temperature, sea ice and the radiation balance during the Arctic summer and appears to simulate around 30% of Arctic warming and sea ice melting over the whole period (1979-2020) and more than 50% over the period 2000 to 2012, which is the fastest Arctic warming decade in the satellite era. In particular, in the summer of 2020, a similar wind pattern reemerged to induce the second-lowest sea ice extent since 1979, suggesting that large scale wind changes in the Arctic is essential in shaping Arctic climate on interannual and interdecadal time scales and may be critical to determine Arctic climate variability in the coming decades. 

The observed upper (0–50 m) Arctic Ocean warming since 1979 has been primarily attributed to anthropogenically driven changes in the high latitudes. Here, using both observational and modeling analyses, we demonstrate that a multiyear trend in the summertime large-scale atmospheric circulation, which we ascribe to internal variability, has played an important role in upper ocean warming in summer and fall over the past four decades due to sea ice-albedo effect induced by atmospheric dynamics. Nudging experiments in which the wind fields are constrained toward the observed state support this mechanism and suggest that the internal variability contribution to recent upper Arctic Ocean warming accounts for up to one quarter of warming over the past four decades and up to 60% of warming from 2000 to 2018. This suggests that climate models need to replicate this important internal process in order to realistically simulate Arctic Ocean temperature variability and trends.

 

Abstract An ice-free Arctic summer is a landmark of global change and has the far-reaching climate, environmental, and economic impacts. However, the Coupled Model Intercomparison Project Phase 6 models’ projected occurrence remains notoriously uncertain. Finding emergent constraints to reduce the projection uncertainties has been a foremost challenge. To establish a physical basis for the constraints, we first demonstrate, with numerical experiments, that the observed trend of Arctic ice loss is primarily driven by the Arctic near-surface air temperature. Thus, two constraints are proposed: the Arctic sea ice sensitivity that measures Arctic sea ice response to the local warming, and the Arctic amplification sensitivity that assesses how well the model responds to anthropogenic forcing and allocates heat to the Arctic region. The two constraints are complementary and nearly scenario-independent. The model-projected first Arctic ice-free year significantly depends on the model’s two climate sensitivities. Thus, the first Arctic ice-free year can be predicted by the linear combination of the two Arctic sensitivity measures. Based on model-simulated sensitivity skills, 20 CMIP models are divided into two equal number groups. The ten realistic-sensitivity models project, with a likelihood of 80%, the ice-free Arctic will occur by additional 0.8 °C global warming from 2019 level or before 2040 under the SSP2-4.5 (medium emission) scenario. The ten realistic-sensitivity models’ spread is reduced by about 70% compared to the ten underestimate-sensitivity models’ large spread. The strategy for creating physics-based emergent constraints through numerical experiments may be instrumental for broad application to other fields for advancing robust projection and understanding uncertainty sources. 

Consensus on the cause of recent midlatitude circulation changes toward a wavier manner in the Northern Hemisphere has not been reached, albeit a number of studies collectively suggest that this phenomenon is driven by global warming and associated Arctic amplification. Here, through a fingerprint analysis of various global simulations and a tropical heating-imposed experiment, we suggest that the suppression of tropical convection along the Inter Tropical Convergence Zone induced by sea surface temperature (SST) cooling trends over the tropical Eastern Pacific contributed to the increased summertime midlatitude waviness in the past 40 years through the generation of a Rossby-wave-train propagating within the jet waveguide and the reduced north-south temperature gradient. This perspective indicates less of an influence from the Arctic amplification on the observed mid-latitude wave amplification than what was previously estimated. This study also emphasizes the need to better predict the tropical Pacific SST variability in order to project the summer jet waviness and consequent weather extremes.

 

Detecting small objects (e.g., manhole covers, license plates, and roadside milestones) in urban images is a long-standing challenge mainly due to the scale of small object and background clutter. Although convolution neural network (CNN)-based methods have made significant progress and achieved impressive results in generic object detection, the problem of small object detection remains unsolved. To address this challenge, in this study we developed an end-to-end network architecture that has three significant characteristics compared to previous works. First, we designed a backbone network module, namely Reduced Downsampling Network (RD-Net), to extract informative feature representations with high spatial resolutions and preserve local information for small objects. Second, we introduced an Adjustable Sample Selection (ADSS) module which frees the Intersection-over-Union (IoU) threshold hyperparameters and defines positive and negative training samples based on statistical characteristics between generated anchors and ground reference bounding boxes. Third, we incorporated the generalized Intersection-over-Union (GIoU) loss for bounding box regression, which efficiently bridges the gap between distance-based optimization loss and area-based evaluation metrics. We demonstrated the effectiveness of our method by performing extensive experiments on the public Urban Element Detection (UED) dataset acquired by Mobile Mapping Systems (MMS). The Average Precision (AP) of the proposed method was 81.71%, representing an improvement of 1.2% compared with the popular detection framework Faster R-CNN. 

Abstract The leading interannual mode of winter surface air temperature over the North American (NA) sector, characterized by a “Warm Arctic, Cold Continents” (WACC) pattern, exerts pronounced influences on NA weather and climate, while its underlying mechanisms remain elusive. In this study, the relative roles of surface boundary forcing versus internal atmospheric processes for the formation of the WACC pattern are quantitatively investigated using a combined analysis of observations and large-ensemble atmospheric global climate model simulations. Internal atmospheric variability is found to play an important role in shaping the year-to-year WACC variability, contributing to about half of the total variance. An anomalous SST pattern resembling the North Pacific Mode is identified as a major surface boundary forcing pattern in driving the interannual WACC variability over the NA sector, with a minor contribution from sea ice variability over the Chukchi- Bering Seas. Findings from this study not only lead to improved understanding of underlying physics regulating the interannual WACC variability, but also provide important guidance for improved modeling and prediction of regional climate variability over NA and the Arctic region. 

Abstract Antarctic sea ice extent (SIE) has slightly increased over the satellite observational period (1979 to the present) despite global warming. Several mechanisms have been invoked to explain this trend, such as changes in winds, precipitation, or ocean stratification, yet there is no widespread consensus. Additionally, fully coupled Earth system models run under historic and anthropogenic forcing generally fail to simulate positive SIE trends over this time period. In this work, we quantify the role of winds and Southern Ocean SSTs on sea ice trends and variability with an Earth system model run under historic and anthropogenic forcing that nudges winds over the polar regions and Southern Ocean SSTs north of the sea ice to observations from 1979 to 2018. Simulations with nudged winds alone capture the observed interannual variability in SIE and the observed long-term trends from the early 1990s onward, yet for the longer 1979–2018 period they simulate a negative SIE trend, in part due to faster-than-observed warming at the global and hemispheric scale in the model. Simulations with both nudged winds and SSTs show no significant SIE trends over 1979–2018, in agreement with observations. At the regional scale, simulated sea ice shows higher skill compared to the pan-Antarctic scale both in capturing trends and interannual variability in all nudged simulations. We additionally find negligible impact of the initial conditions in 1979 on long-term trends.