Browse

You are looking at 101 - 110 of 12,990 items for :

  • Journal of Climate x
  • Refine by Access: All Content x
Clear All
Free access
Dong Wan Kim
,
Sukyoung Lee
,
Joseph P. Clark
, and
Steven B. Feldstein

Abstract

A thermodynamic energy budget analysis is applied to the lowest model level of the ERA5 dataset to investigate the mechanisms that drive the growth and decay of extreme positive surface air temperature (SAT) events. Regional and seasonal variation of the mechanisms are investigated. For each grid point on Earth’s surface, a separate composite analysis is performed for extreme SAT events, which are days when temperature anomaly exceeds the 95th percentile. Among the dynamical terms, horizontal temperature advection of the climatological temperature by the anomalous wind dominates SAT anomaly growth over the extratropics, while nonlinear horizontal temperature advection is a major factor over high-latitude regions and the adiabatic warming is important over major mountainous regions. During the decay period, advection of the climatological temperature by the anomalous wind sustains the warming while nonlinear advection becomes the dominant decay mechanism. Among diabatic heating processes, vertical mixing contributes to the SAT anomaly growth over most locations while longwave radiative cooling hinders SAT anomaly growth, especially over the ocean. However, over arid regions during summer, longwave heating largely contributes to SAT anomaly growth while the vertical mixing dampens the SAT anomaly growth. During the decay period, both longwave cooling and vertical mixing contribute to SAT anomaly decay with more pronounced effects over the ocean and land, respectively. These regional and seasonal characteristics of the processes that drive extreme SAT events can serve as a benchmark for understanding the future behavior of extreme weather.

Open access
Zihan Song
,
Shang-Ping Xie
,
Lixiao Xu
,
Xiao-Tong Zheng
,
Xiaopei Lin
, and
Yu-Fan Geng

Abstract

A deep winter mixed layer forms north of the Antarctic Circumpolar Current (ACC) in the Indo-Pacific sectors, while the mixed layer depth (MLD) is shallow in the Atlantic. Using observations and a global atmospheric model, this study investigates the contribution of surface buoyancy flux and background stratification to inter-basin MLD variations. The surface heat flux is decomposed into broad-scale and frontal-scale variations. At the broad-scale, the meandering ACC path is accompanied by a zonal wavenumber-1 structure of sea surface temperature with a warmer Pacific than Atlantic; under the prevailing westerly winds, this temperature contrast results in larger surface heat loss facilitating deeper MLD in the Indo-Pacific sectors than the Atlantic. In the Indian sector, the intense ACC fronts strengthen surface heat loss compared to the Pacific. The surface freshwater flux pattern largely follows that of evaporation and reinforces the heat flux pattern, especially in the southeast Pacific. A diagnostic relationship is introduced to highlight the role of ACC’s sloping isopycnals in setting a weak sub-mixed-layer stratification north of ACC. This weak stratification varies in magnitude across basins. In the Atlantic and western Indian oceans where the ACC is at a low latitude (∼45°S), solar heating, intrusions of subtropical gyres and energetic mesoscale eddies together maintain relatively strong stratification. In the southeast Pacific, in comparison, the ACC reaches the southernmost latitude (56°S), far away from the subtropical front. This creates weaker stratification allowing deep mixed layers to form, aided by surface buoyancy loss.

Restricted access
Duo Chan
,
Geoffrey Gebbie
, and
Peter Huybers

Abstract

Land surface air temperatures (LSAT) inferred from weather station data differ among major research groups. The estimate by NOAA’s monthly Global Historical Climatology Network (GHCNm) averages 0.02°C cooler between 1880 and 1940 than Berkeley Earth’s and 0.14°C cooler than the Climate Research Unit estimates. Such systematic offsets can arise from differences in how poorly documented changes in measurement characteristics are detected and adjusted. Building upon an existing pairwise homogenization algorithm used in generating the fourth version of NOAA’s GHCNm(V4), PHA0, we propose two revisions to account for autocorrelation in climate variables. One version, PHA1, makes minimal modification to PHA0 by extending the threshold used in breakpoint detection to be a function of LSAT autocorrelation. The other version, PHA2, uses penalized likelihood to detect breakpoints through optimizing a model-selection problem globally. To facilitate efficient optimization for series with more than 1000 time steps, a multiparent genetic algorithm is proposed for PHA2. Tests on synthetic data generated by adding breakpoints to CMIP6 simulations and realizations from a Gaussian process indicate that PHA1 and PHA2 both similarly outperform PHA0 in recovering accurate climatic trends. Applied to unhomogenized GHCNmV4, both revised algorithms detect breakpoints that correspond with available station metadata. Uncertainties are estimated by perturbing algorithmic parameters, and an ensemble is constructed by pooling 50 PHA1- and 50 PHA2-based members. The continental-mean warming in this new ensemble is consistent with that of Berkeley Earth, despite using different homogenization approaches. Relative to unhomogenized data, our homogenization increases the 1880–2022 trend by 0.16 [0.12, 0.19]°C century−1 (95% confidence interval), leading to continental-mean warming of 1.65 [1.62, 1.69]°C over 2010–22 relative to 1880–1900.

Significance Statement

Accurately correcting for systematic errors in observational records of land surface air temperature (LSAT) is critical for quantifying historical warming. Existing LSAT estimates are subject to systematic offsets associated with processes including changes in instrumentation and station movement. This study improves a pairwise homogenization algorithm by accounting for the fact that climate signals are correlated over time. The revised algorithms outperform the original in identifying discontinuities and recovering accurate warming trends. Applied to monthly station temperatures, the revised algorithms adjust trends in continental mean LSAT since the 1880s to be 0.16°C century−1 greater relative to raw data. Our estimate is most consistent with that from Berkeley Earth and indicates lesser and greater warming than estimates from NOAA and the Met Office, respectively.

Restricted access
Sandrine Trotechaud
,
Bruno Tremblay
,
James Williams
,
Joy Romanski
,
Anastasia Romanou
,
Mitchell Bushuk
,
William Merryfield
, and
Rym Msadek

Abstract

Observations show predictive skill of the minimum sea ice extent (Min SIE) from late winter anomalous offshore ice drift along the Eurasian coastline, leading to local ice thickness anomalies at the onset of the melt season—a signal then amplified by the ice–albedo feedback. We assess whether the observed seasonal predictability of September sea ice extent (Sept SIE) from Fram Strait Ice Area Export (FSIAE; a proxy for Eurasian coastal divergence) is present in global climate model (GCM) large ensembles, namely the CESM2-LE, GISS-E2.1-G, FLOR-LE, CNRM-CM6-1, and CanESM5. All models show distinct periods where winter FSIAE anomalies are negatively correlated with the May sea ice thickness (May SIT) anomalies along the Eurasian coastline, and the following Sept Arctic SIE, as in observations. Counterintuitively, several models show occasional periods where winter FSIAE anomalies are positively correlated with the following Sept SIE anomalies when the mean ice thickness is large, or late in the simulation when the sea ice is thin, and/or when internal variability increases. More important, periods with weak correlation between winter FSIAE and the following Sept SIE dominate, suggesting that summer melt processes generally dominate over late-winter preconditioning and May SIT anomalies. In general, we find that the coupling between the winter FSIAE and ice thickness anomalies along the Eurasian coastline at the onset of the melt season is a ubiquitous feature of GCMs and that the relationship with the following Sept SIE is dependent on the mean Arctic sea ice thickness.

Restricted access
Ye-Jun Jun
,
Seok-Woo Son
,
Hera Kim
,
Hyo-Seok Park
, and
Jin-Ho Yoon

Abstract

Concurrent with global warming, the Eurasian continent has experienced frequent cold winters in recent decades. Although still debatable, such Eurasian winter cooling, which was particularly pronounced in the late 20th century, has been attributed to internal climate variability, the process of which remains elusive. By examining multi-model large ensemble simulations, this study explores the potential sources of internal climate variability responsible for the Eurasian winter cooling trend over 1987–2006. Model simulations show a large ensemble spread in the Eurasian winter temperature trend with an ensemble mean close to zero. A comparison of the ensemble members shows that a circulation pattern favorable for the Eurasian cooling is characterized by the anticyclonic and cyclonic enhancements of seal level pressure (SLP) trend in the sub-Arctic and Aleutian regions, respectively. This dipolar SLP trend is closely related to the deep Arctic warming, the change in midlatitude snow cover, and the enhancement of atmospheric convection over the tropical western Pacific. This result suggests that the Eurasian winter cooling is likely associated not only to the changes in mid- to high-latitude conditions but also to the changes in tropical convection. The possible mechanism of the tropically-induced Aleutian low deepening is also discussed.

Restricted access
Zunya Wang
,
Xingwen Jiang
,
Zongjian Ke
, and
Yafang Song

Abstract

The related atmospheric and oceanic factors are investigated in this analysis to understand the natural attributes responsible for the significant increase of the high temperature extremes (HTEs) on the Tibetan Plateau (TP) in summer. It is found that a stronger-than-normal South Asian high (SAH) and corresponding weaker-than-normal East Asian jet, an anomalous anticyclone and intensified midlevel westerly wind over the TP, and a more extensive, stronger, farther westward- and northward-stretching western Pacific subtropical high motivate more occurrences of HTEs over the TP on the interannual time scale. From 1961 to 2021, these crucial circulation patterns show a significant changing trend favorable for the occurrence of HTEs and thus contribute to its great increase. Further, the significant warmings of sea surface temperature (SST) in the tropical western Indian, northern North Pacific, and western North Atlantic Oceans make great contributions through different air–sea interactive processes as the Matsuno–Gill response, zonal vertical circulation cell, and mid- to high-latitude teleconnection wave train, respectively. Meanwhile, the interdecadal variability plays an important role. A breakpoint at the early twenty-first century is detected in the occurrence of summer HTEs on the TP. Both the crucial circulation patterns and the SST anomalies in the key oceanic regions experienced significant interdecadal transition to favor the occurrence of HTEs. In particular, the Atlantic multidecadal oscillation (AMO) is significantly and positively correlated with the interdecadal variation of summer HTEs on the TP. The zonal teleconnection wave train triggered by AMO forms a stronger-than-normal SAH and strengthened midlevel westerly airflow over the TP, conducive to the increase of summer HTEs on the TP.

Restricted access
Tong Shen
and
Riyu Lu

Abstract

This study investigates the relationship between the uncertainty of empirical orthogonal function (EOF) modes and sampling size in climate models, using simulated results of preindustrial control (piControl) experiments in phase 6 of the Coupled Model Intercomparison Project (CMIP6), and taking the North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO) as examples. The results indicate that this relationship can be quantified by a concise fitting function [i.e., y = a/(xb)]. Here, y is the 5%–95% confidence interval of congruence coefficient, x is the sampling size, and a and b are two parameters depending on models or observations. As compared with b, which modulates the sampling size in the fitting function, the parameter a scales the sampling size and thus plays a much more important role. Further analysis indicates that the parameter a, or the uncertainty of EOF1 mode, decreases dramatically with the increase of the difference between variance fractions of EOF1 and EOF2 modes, approximately in the form of a power function. The minimum sampling size to ensure a reliable EOF mode can also be estimated by the fitting function and shows a great diversity among models both for the NAO and ENSO. The diversity suggests the importance of estimating the minimum sampling size before model evaluations on climate variability modes and projections on the future change in modes, particularly when the EOF2 mode explains the variance close to EOF1 mode.

Significance Statement

Empirical orthogonal function (EOF) analysis, principal component analysis, or eigenvector analysis has been widely used in various research fields. However, it remains as an open question as to how large the sampling size is required to be to obtain reliable modes through the EOF method. In this study, we investigate the relationship between the uncertainty of EOF results and sampling size in current climate models, using adequately long simulated data, and we find that this relationship can be depicted by the fitting function y = a/(xb). Here, y represents the uncertainty, x is the sampling size, and a and b are parameters. The parameter a is closely related to the difference between variance fractions of first and second EOF modes and plays a more important role in the fitting function. The minimum sampling sizes that are required to obtain reliable EOF modes can also be estimated by the fitting function and vary greatly from model to model. The results provide a basis for judging the reliability of EOF modes, particularly when the first and second EOF modes explain similar variance fractions.

Restricted access
Sizhuo Wei
,
Pang-Chi Hsu
, and
Jinhui Xie

Abstract

The time of rainy season onset is crucial information for policymakers, especially in densely populated regions such as the Yangtze River basin (YRB) in China. In this study, we proposed a new grid-based index to objectively detect mei-yu onset timing using reanalysis data and model predictions, and then we identified the key processes via which intraseasonal oscillation (ISO) affects the YRB mei-yu onset and its subseasonal predictability based on scale-decomposed moisture analysis. Climatologically, propagation of an ISO anticyclonic anomaly toward East China supports the moisture convergence required for rainy season onset over the YRB via interaction with the seasonal-mean moisture component. In the years of early mei-yu onset, the ISO was enhanced earlier in May and favored the moisture convergence anomaly in late May–early June, when the mei-yu started. In contrast, the enhanced ISO and associated moistening processes were observed later in June–early July in the years with delayed onset. The European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction models show skillful prediction of mei-yu onset at forecast lead times of 5–6 pentads, whereas the China Meteorological Administration model has limited skill of 3 pentads. The differences in model prediction skill are related to the accuracy of predicted moisture convergence anomalies induced by the ISO. The prediction bias in mei-yu onset timing (early or delayed) is also connected to bias in the occurrence timing of enhanced intraseasonal perturbations, suggesting the vital role of ISO in YRB mei-yu onset on the subseasonal time scale.

Restricted access
Andrew Hoell
,
Xiao-Wei Quan
,
Rachel Robinson
, and
Martin Hoerling

Abstract

Potential predictability of two-year droughts indicated by low runoff in consecutive April-September seasons in the Upper (UMRB) and Lower (LMRB) Missouri River Basin are examined with observed estimates and climate models. The majority of annual runoff is generated in April-September, which is also the main precipitation and evapotranspiration season. Physical features related to low April-September runoff in both UMRB and LMRB include a dry land surface state indicated by low soil moisture, low snowpack indicated by low snow water equivalent, and a wave train across the Pacific-North American region that can be generated internally by the atmosphere or forced by the La Niña phase of the El Niño-Southern Oscillation. When present in March, these features increase the risk of low runoff in the following April-September warm seasons. Antecedent low soil moisture significantly increases low runoff risks in each of the following two April-September, as the dry land surfaces decrease runoff efficiency. Initial low snow water equivalent, especially in the Missouri River headwaters of Montana, generates less runoff in the subsequent warm season. La Niña increases the risk of low runoff during the warm seasons by suppressing precipitation via dynamical-induced atmospheric circulation anomalies. Model simulations that differ in their radiative forcing suggest that climate change increases the predictability of two-year droughts in the Missouri River Basin related to La Niña. The relative risk of low runoff in the second April-September following a La Niña event in March is greater in the presence of stronger radiative forcing.

Restricted access