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Yuting Yang
,
Xiaopeng Cui
,
Ying Li
,
Lijun Huang
, and
Jia Tian

Abstract

The northeast cold vortex (NECV) is an essential system in the northeast region (NER) of China. Understanding the moisture source and associated transport characteristics of NECV rainstorms is the key to the knowledge of its mechanisms. In this study, we focus on two NECV rainstorm centers during the warm season (May–September) from 2008 to 2013. The Flexible Particle (FLEXPART) model and quantitative contribution analysis method are applied to reveal the moisture sources and their quantitative contribution. The results demonstrate that for the northern NECV rainstorm center (R1), Northeast Asia (35.66%) and east-central China and its coastal regions (29.14%) make prominent moisture contributions, followed by R1 (11.37%), whereas east-central China and its coastal regions (45.16%), the southern NECV rainstorm center itself (R2, 17.90%), and the northwest Pacific (10.24%) principally contribute to R2. Moisture uptake in Northeast Asia differs between R1 and R2, which could serve as one of the vital indicators to judge where the NECV rainstorm falls in NER. Moisture from the Arabian Sea, the Bay of Bengal, and the South China Sea suffers massive en route loss, although these sources’ contribution and uptake are positively correlated with the intensity and scale of NECV rainstorms in the two centers. There exists intermonth and geographical variability in NECV rainstorms when the main moisture source region contributes the most. Regulated by the atmospheric circulation and the East Asian summer monsoon, the particle trajectories and source contributions of NECV rainstorms vary from month to month. Sources’ contribution also turns out to be diverse in the overall warm season.

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Tingting Zhu
and
Jin-Yi Yu

Abstract

Utilizing a 2200-yr CESM1 preindustrial simulation, this study examines the influence of property distinctions between single-year (SY) and multiyear (MY) La Niñas on their respective impacts on winter surface air temperatures across mid–high-latitude continents in the model, focusing on specific teleconnection mechanisms. Distinct impacts were identified in four continent sectors: North America, Europe, Western Siberia (W-Siberia), and Eastern Siberia (E-Siberia). The typical impacts of simulated SY La Niña events are featured with anomalous warming over Europe and W&E-Siberia and anomalous cooling over North America. Simulated MY La Niña events reduce the typical anomalous cooling over North America and the typical anomalous warming over W&E-Siberia but intensify the typical anomalous warming over Europe. The distinct impacts of simulated MY La Niñas are more prominent during their first winter than during the second winter, except over W-Siberia, where the distinct impact is more pronounced during the second winter. These overall distinct impacts in the CESM1 simulation can be attributed to the varying sensitivities of these continent sectors to the differences between MY and SY La Niñas in their intensity, location, and induced sea surface temperature anomalies in the Atlantic Ocean. These property differences were linked to the distinct climate impacts through the Pacific North America, North Atlantic Oscillation, Indian Ocean–induced wave train, and tropical North Atlantic–induced wave train mechanisms. The modeling results are then validated against observations from 1900 to 2022 to identify disparities in the CESM1 simulation.

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Anna Lo Piccolo
,
Christopher Horvat
, and
Baylor Fox-Kemper

Abstract

During polar winter, refreezing of exposed ocean areas results in the rejection of brine, i.e., salt-enriched plumes of water, a source of available potential energy that can drive ocean instabilities. As this process is highly localized, and driven by sea ice physics, not gradients in oceanic or atmospheric buoyancy, it is not currently captured in modern climate models. This study aims to understand the energetics and lateral transfer of density at a semi-infinite, instantaneously opened, and continuously refreezing sea ice edge through a series of high-resolution model experiments. We show that kilometer-scale submesoscale eddies grow from baroclinic instabilities via an inverse energy cascade. These eddies meander along the ice edge and propagate laterally. The lateral transfer of buoyancy by eddies is not explained by existing theories. We isolate the fundamental forcing-independent quantities driving lateral mixing and discuss the implications for the overall strength of submesoscale activity in the Arctic Ocean.

Open access
Kelsey Emard
,
Olivia Cameron
,
William R. Wieder
,
Danica L. Lombardozzi
,
Rebecca Morss
, and
Negin Sobhani

Abstract

This paper analyzes findings from semistructured interviews and focus groups with 31 farmers in the Willamette Valley in which farmers were asked about their needs for climate data and about the usability of a range of outputs from the Community Earth System Model, version 2 (CESM2), for their soil management practices. Findings indicate that climate and soils data generated from CESM and other Earth system models (ESMs), despite their coarse spatial scale resolutions, can inform farmers’ long-term decisions, but that the data would be more usable if the outputs were provided in a format that allowed farmers to choose the variables and thresholds relevant to their particular needs and if ESMs incorporated farmer practices including residue removal, cover cropping, and tillage levels into the model operations so that farmers could better understand the impacts of their decisions. Findings also suggest that although there is a significant gap in the spatial resolution at which these global ESMs generate data and the spatial resolution needed by farmers to make most decisions, farmers are adept at making scalar adjustments to apply coarse-resolution data to the specifics of their own farm’s microclimate. Thus, our findings suggest that, to support agricultural decision-making, development priorities for ESMs should include developing better representations of agricultural management practices within the models and creating interactive data dashboards or platforms.

Open access
Viktor Gouretski
,
Fabien Roquet
, and
Lijing Cheng

Abstract

The study focuses on biases in ocean temperature profiles obtained by means of Satellite Relay Data Loggers (SRDL recorders) and time–depth recorder (TDR) attached to marine mammals. Quasi-collocated profiles from Argo floats and from ship-based conductivity–temperature–depth (CTD) profilers are used as reference. SRDL temperature biases depend on the sensor type and vary with depth. For the most numerous group of Valeport 3 (VP3) and conductivity–temperature–fluorescence (CTF) sensors, the bias is negative except for the layer 100–200 m. The vertical bias structure suggests a link to the upper-ocean thermal structure within the upper 200-m layer. Accounting for a time lag which might remain in the postprocessed data reduces the bias variability throughout the water column. Below 200-m depth, the bias remains negative with the overall mean of −0.027° ± 0.07°C. The suggested depth and thermal corrections for biases in SRDL data are within the uncertainty limits declared by the manufacturer. TDR recorders exhibit a different bias pattern, showing the predominantly positive bias of 0.08°–0.14°C below 100 m primarily due to the systematic error in pressure.

Significance Statement

The purpose of this work is to improve the consistency of the data from the specific instrumentation type used to measure ocean water temperature, namely, the data from miniature temperature sensors attached to marine mammals. As mammals dive during their route to and from their feeding areas, these sensors measure water temperature and dataloggers send the measured temperature data to oceanographic data centers via satellites as soon as the mammals return to the sea surface. We have shown that these data exhibit small systematic instrumental errors and suggested the respective corrections. Taking these corrections into account is important for the assessment of the ocean climate change.

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F. Guo
,
S. C. Clemens
,
X. Du
,
X. Liu
,
Y. Liu
,
J. Sun
,
H. Fan
,
T. Wang
, and
Y. Sun

Abstract

Millennial-scale climate change is thought to be synchronous throughout the Northern Hemisphere and has been demonstrated to be strongly modulated by longer-term glacial–interglacial and orbital-scale processes. However, processes that modulate the magnitude of millennial-scale variability (MMV) at the glacial–interglacial time scale remain unclear. We present multiproxy evidence showing out-of-phase relationships between the MMV of East Asian and North Atlantic climate proxies at the eccentricity band. During most late Pleistocene glacial intervals, the MMV in North Atlantic SST and East Asian monsoon (EAM) proxies shows a gradual weakening trend from glacial inceptions into glacial maxima, inversely proportional to that of the North Atlantic ice-rafted detritus record. The inverse glacial age trends apply to both summer and winter monsoon proxies across the loess, speleothem, and marine archives, indicating fundamental linkages between MMV records of the North Atlantic and East Asia. We infer that intensified glacial age iceberg discharge is accompanied by weakened Atlantic meridional overturning circulation via changes in freshwater input and water column stability, leading to a reduction in North Atlantic SST and wind anomalies, subsequently propagating dampened millennial-scale variability into the midlatitude East Asian monsoon region via the westerlies. Our results indicate that the impact of North Atlantic iceberg discharge and the associated variability in water column stability at the millennial scale is a primary influence on hydroclimate instability in East Asia.

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Lingyu Zhou
,
Yan Xia
,
Fei Xie
,
Chen Zhou
, and
Chuanfeng Zhao

Abstract

The variability of stratospheric water vapor (SWV) plays a crucial role in stratospheric chemistry and Earth’s energy budget, strongly influenced by sea surface temperature (SST). In this study, we systematically investigate the response of lower-SWV (LSWV) to regional sea surface temperature changes using idealized SST patch experiments within a climate model. The results indicate that LSWV is most sensitive to tropical sea surface temperature, with the strongest response occurring in late autumn and early winter. Warming of the tropical Indian Ocean and western Pacific (WP) leads to stratospheric drying, while warming of the tropical Atlantic (TA) and eastern Pacific results in stratospheric moistening. The drying impact on LSWV due to warming in the western Pacific Ocean exceeds the wet effect in the eastern Pacific Ocean by approximately 60%. The variations in tropical SST influence LSWV by modulating the temperature at the tropical tropopause layer, especially over the Indo-Pacific warm pool through Matsuno–Gill responses. Furthermore, the response of LSWV to tropical SST changes exhibits nonnegligible nonlinearity, which indicates the importance of nonlinearity in determining the LSWV response to global surface warming.

Significance Statement

In this study, we explore how changes in the temperature of the ocean’s surface can affect the amount of water vapor in the stratosphere, a layer of Earth’s atmosphere. Understanding this relationship is important because water vapor in the stratosphere can influence both our climate and the chemistry of the atmosphere. Using a climate model, we found that water vapor in the lower stratosphere is especially responsive to temperature changes in tropical ocean regions. Specifically, when the Indian Ocean and the western Pacific get warmer, the stratosphere tends to get drier. On the other hand, warming in the Atlantic and eastern Pacific leads to more moisture in the stratosphere. The way these changes add up is complex and not simply a sum of individual parts, especially in tropical warm pool regions. Our findings have implications for how we understand and predict the impacts of climate change on stratospheric water vapor.

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Abby Hutson
,
Ayumi Fujisaki-Manome
, and
Brent Lofgren

Abstract

The Weather Research and Forecasting (WRF) Model is used to dynamically downscale ERA-Interim global reanalysis data to test its performance as a regional climate model (RCM) for the Great Lakes region (GLR). Four cumulus parameterizations and three spectral nudging techniques applied to moisture are evaluated based on 2-m temperature and precipitation accumulation in the Great Lakes drainage basin (GLDB). Results are compared to a control simulation without spectral nudging, and additional analysis is presented showing the contribution of each nudged variable to temperature, moisture, and precipitation. All but one of the RCM test simulations have a dry precipitation bias in the warm months, and the only simulation with a wet bias also has the least precipitation error. It is found that the inclusion of spectral nudging of temperature dramatically improves a cold-season cold bias, and while the nudging of moisture improves simulated annual and diurnal temperature ranges, its impact on precipitation is complicated.

Significance Statement

Global climate models are vital to understanding our changing climate. While many include a coarse representation of the Great Lakes, they lack the resolution to represent effects like lake effect precipitation, lake breeze, and surface air temperature modification. Therefore, using a regional climate model to downscale global data is imperative to correctly simulate the land–lake–atmosphere feedbacks that contribute to regional climate. Modeling precipitation is particularly important because it plays a direct role in the Great Lakes’ water cycle. The purpose of this study is to identify the configuration of the Weather Research and Forecasting Model that best simulates precipitation and temperature in the Great Lakes region by testing cumulus parameterizations and methods of nudging the regional model toward the global model.

Open access
Shuang Yu
,
Indrasis Chakraborty
,
Gemma J. Anderson
,
Donald D. Lucas
,
Yannic Lops
, and
Daniel Galea

Abstract

Precipitation values produced by climate models are biased due to the parameterization of physical processes and limited spatial resolution. Current bias-correction approaches usually focus on correcting lower-order statistics (mean and standard deviation), which make it difficult to capture precipitation extremes. However, accurate modeling of extremes is critical for policymaking to mitigate and adapt to the effects of climate change. We develop a deep learning framework, leveraging information from key dynamical variables impacting precipitation to also match higher-order statistics (skewness and kurtosis) for the entire precipitation distribution, including extremes. The deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Using climate model simulation data and observations that are climatologically similar but not strictly paired, the UFNet identifies and corrects the climate model biases, significantly improving the estimation of daily precipitation as measured by a broad range of spatiotemporal statistics. In particular, UFNet significantly improves the underestimation of extreme precipitation values seen with current bias-correction methods. Our approach constitutes a generalized framework for correcting other climate model variables which improves the accuracy of the climate model predictions, while utilizing a simpler and more stable training process.

Open access
Ying Dai
,
Peter Hitchcock
, and
Isla R. Simpson

Abstract

This study evaluates the representation of the composite-mean surface response to sudden stratospheric warmings (SSWs) in 28 CMIP6 models. Most models can reproduce the magnitude of the SLP response over the Arctic, although the simulated Arctic SLP response varies from model to model. Regarding the structure of the SLP response, most models exhibit a basin-symmetric negative Northern Annular Mode (NAM)-like response with a cyclonic Pacific SLP response, whereas the reanalysis shows a highly basin-asymmetric negative NAO-like response without a robust Pacific center. We then explore the drivers of these model biases and spread by applying a multiple linear regression (MLR). The results show that the polar cap temperature anomalies at 100 hPa (ΔT 100) modulate the magnitude of both the Arctic SLP response and the cyclonic Pacific SLP response. Apart from ΔT 100, the intensity and latitudinal location of the climatological eddy-driven jet in the troposphere also affect the magnitude of the Arctic SLP response. The compensation of model biases in these two tropospheric metrics and the good model representation of ΔT 100 explain the good agreement between the ensemble mean and the reanalysis on the magnitude of the Arctic SLP response, as indicated by the fact that the ensemble mean lies well within the reanalysis uncertainty range and that the reanalysis mean sits well within the model distribution. The Niño-3.4 SST anomalies and North Pacific SST dipole anomalies together with ΔT 100 modulate the cyclonic Pacific SLP response. In this case, biases in both oceanic drivers work in the same direction and lead to the cyclonic Pacific SLP response in models that are not present in the reanalysis.

Significance Statement

Sudden stratospheric warmings (SSWs) represent an important source of skill for forecasting winter weather on subseasonal-to-seasonal time scales. To what extent SSWs could be used to improve the prediction of surface weather depends on how well stratosphere–troposphere coupling associated with SSWs is represented in climate models. Therefore, we evaluate the representation of stratosphere–troposphere coupling associated with SSWs in 28 state-of-the-art climate models. The representation is found to diverge widely among climate models, and some are biased noticeably from the reanalysis. The models’ spread and bias are largely driven by five major factors and can be reduced substantially by making bias corrections to these factors.

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