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Ziping Zuo
,
Jimmy C. H. Fung
,
Zhenning Li
,
Yiyi Huang
,
Mau Fung Wong
,
Alexis K. H. Lau
, and
Xingcheng Lu

Abstract

Recent worldwide heatwaves have shattered temperature records in many regions. In this study, we applied a dynamical downscaling method on the high-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-1-2-HR) to obtain projections of the summer thermal environments and heatwaves in the Pearl River Delta (PRD) considering three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in the middle and late twenty-first century. Results indicated that relative to the temperatures in the 2010s, the mean increases in the summer (June–September) daytime and nighttime temperatures in the 2040s will be 0.7°–0.8°C and 0.9°–1.1°C, respectively. In the 2090s, the mean difference will be 0.5°–3.1°C and 0.7°–3.4°C, respectively. SSP1-2.6 is the only scenario in which the temperatures in the 2090s are expected to be lower than those in the 2040s. When compared with those in the 2010s, hot extremes are expected to be more frequent, intense, extensive, and longer-lasting in the future in the SSP2-4.5 and SSP5-8.5 scenarios. In the 2010s, a heatwave occurred in the PRD lasted for 6 days on average, with a mean daily maximum temperature of 34.4°C. In the 2040s, the heatwave duration and intensity are expected to increase by 2–3 days and 0.2°–0.4°C in all three scenarios. In the 2090s, these values will become 23 days and 36.0°C in SSP5-8.5. Moreover, a 10-yr extreme high temperature in the 2010s is expected to occur at a monthly frequency from June to September in the 2090s.

Significance Statement

Pearl River Delta (PRD) has been experiencing record-shattering heatwaves in recent years. This study aims to investigate the future trends of summer heatwaves in the PRD by modeling three future scenarios including a sustainable scenario, an intermediate scenario, and a worst-case scenario. Except for the sustainable scenario, summer temperatures in the intermediate and worst-case scenarios will keep increasing, and heatwaves will become more frequent, intense, extensive, and longer-lasting. In the worst-case scenario, extreme heat events that occurred once in 10 years in the 2010s will shorten to once a month in the 2090s. A better understanding of heatwave trends will benefit implementing climate mitigation methods, urban planning, and improving social infrastructure.

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Vahid Nourani
,
Kasra Khodkar
,
Aida Hosseini Baghanam
,
Sameh Ahmed Kantoush
, and
Ibrahim Demir

Abstract

This study investigated the uncertainty involved in statistical downscaling of hydroclimatic time series obtained by artificial neural networks (ANNs). Phase 6 of the Coupled Model Intercomparison Project (CMIP6) general circulation model (GCM) Canadian Earth System Model, version 5 (CanESM5), was used as large-scale predictor data for downscaling temperature and precipitation parameters. Two ANNs, feed forward and long short-term memory (LSTM), were utilized for statistical downscaling. To quantify the uncertainty of downscaling, prediction intervals (PIs) were estimated via the lower upper bound estimation (LUBE) method. To assess performance of proposed models in different climate regimes, data from the Tabriz and Rasht stations in Iran were employed. The calibrated models via historical GCM data were used for future projections via the high-forcing and fossil fuel–driven development scenario shared socioeconomic pathway (SSP) 5-8.5. Projections were compared with the Canadian Regional Climate Model 4 (Can-RCM4) projections via the same scenario. Results indicated that both LSTM-based point predictions and PIs are more accurate than the feedforward neural network (FFNN)-based predictions, with an average of 55% higher Nash–Sutcliffe efficiency (NSE) for point predictions and 25% lower coverage width criterion (CWC) for PIs. Projections suggested that Tabriz is going to experience a warmer climate with an increase in average temperature of 2° and 5°C for near and far futures, respectively, and a drier climate with a 20% decrease in precipitation until 2100. Future projections for the Rasht station, however, suggested a more uniform climate with less seasonal variability. Average precipitation will increase by up to 25% and 70% until near and far future periods, respectively. Ultimately, point predictions show that the average temperature in Rasht will increase by 1°C until the near future and then be a constant average temperature until the far future.

Significance Statement

The downscaling of hydroclimatic parameters is subject to uncertainty. The best way is to provide an area with the highest contingency of them as a prediction interval. The reduction width of such an interval leads to increased confidence in explaining and predicting these processes. We proposed and applied a deep learning–based machine learning method for both point prediction and prediction interval estimation of temperature and precipitation parameters for the future over two different climatic regions. The results show the superiority of such a machine learning–based prediction interval estimation for quantification of the downscaling uncertainty.

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Yen-Chao Chiu
and
Fang-Ching Chien

Abstract

This study investigates the characteristics and long-term trends of southwesterly flows around southern Taiwan (SWs) during mei-yu seasons (15 May–15 June) from 1979 to 2022. The results show that the number of SWs in general exhibited an increasing trend over this 44-yr period, with a decadal oscillation starting from a relatively small number in the 1980s and reaching a relative peak in the 2000s. This tendency posts a potential threat to Taiwan because of the increasing trend of heavy rainfall associated with the higher moisture flux of the SWs events. The SWs activity was influenced by the long-term increasing trend of geopotential height gradients and their decadal variability near Taiwan. When the intraseasonal oscillation was evident, the weather system mainly affecting the occurrence of SWs was the low pressure system to the north of Taiwan; when it was weak, the intensity and location of the western North Pacific subtropical high to the south of Taiwan was relatively more important. In addition, the SWs index, which was highly correlated with the precipitation during mei-yu seasons, can effectively reflect the interannual variability of precipitation in Taiwan in periods of different lengths. These findings indicate that the SWs index can be used as a monsoonal precipitation index for Taiwan, especially southern Taiwan.

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Julia F. Lockwood
,
Nick Dunstone
,
Leon Hermanson
,
Geoffrey R. Saville
,
Adam A. Scaife
,
Doug Smith
, and
Hazel E. Thornton

Abstract

North Atlantic Ocean hurricane activity exhibits significant variation on multiannual time scales. Advance knowledge of periods of high activity would be beneficial to the insurance industry as well as society in general. Previous studies have shown that climate models initialized with current oceanic and atmospheric conditions, known as decadal prediction systems, are skillful at predicting North Atlantic hurricane activity averaged over periods of 2–10 years. We show that this skill also translates into skillful predictions of real-world U.S. hurricane damage. Using such systems, we have developed a prototype climate service for the insurance industry giving probabilistic forecasts of 5-yr-mean North Atlantic hurricane activity, measured by the total accumulated cyclone energy (ACE index), and 5-yr-total U.S. hurricane damage (given in U.S. dollars). Rather than tracking hurricanes in the decadal systems directly, the forecasts use a relative temperature index known to be strongly linked to hurricane activity. Statistical relationships based on past forecasts of the index and observed hurricane activity and U.S. damage are then used to produce probabilistic forecasts. The predictions of hurricane activity and U.S. damage for the period 2020–24 are high, with ∼95% probabilities of being above average. We note that skill in predicting the temperature index on which the forecasts are based has declined in recent years. More research is therefore needed to understand under which conditions the forecasts are most skillful.

Significance Statement

The purpose of this article is to explain the science and methods behind a recently developed prototype climate service that uses initialized climate models to give probabilistic forecasts of 5-yr-mean North Atlantic Ocean hurricane activity, as well as 5-yr-total associated U.S. hurricane damage. Although skill in predicting North Atlantic hurricane activity on this time scale has been known for some time, a key result in this article is showing that this also leads to predictability in real-world damage. These forecasts could be of benefit to the insurance industry and to society in general.

Open access
AMS Publications Commission AMS Publications Commission
Open access
Michael T. Kiefer
,
Jeffrey A. Andresen
,
Deborah G. McCullough
,
James B. Wieferich
,
Justin Keyzer
, and
Steve A. Marquie

Abstract

Gridded climate datasets are used by researchers and practitioners in many disciplines, including forest ecology, agriculture, and entomology. However, such datasets are generally unable to account for microclimatic variability, particularly within sites or among individual trees. One such dataset is a recent climatology of extreme minimum temperatures in the U.S. Great Lakes region, based on the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) gridded temperature dataset. Development of this climatology was motivated by interest in the spatiotemporal variability of winter temperatures potentially lethal to the hemlock woolly adelgid (HWA) (Adelges tsugae Annand) (Hemiptera: Adelgidae), an invasive insect that causes mortality of eastern hemlock (Tsuga canadensis). In this study, cold-season daily minimum temperatures were monitored at six Michigan sites varying in latitude, elevation, Great Lakes proximity, and HWA infestation status, to address two objectives. First, we documented the spatiotemporal variability in daily minimum air temperatures recorded at multiple aspects and heights on selected hemlock trees. Second, this variability was characterized in the context of the PRISM extreme minimum temperature climatology. Tree-sensor air temperatures exhibited minimal relationships with aspect but considerable sensitivity to height. Daily minimum temperatures were higher for some tree sensors positioned ≤ 0.2 m above ground level during some time periods, with overall muted temporal variability, relative to an adjacent ambient sensor. This phenomenon was attributed to the insulating effects of snow cover, because the tree–ambient sensor temperature difference was positively correlated with snow depth. Overall, results indicate that such unresolved variability warrants consideration by gridded climate dataset users.

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Fei Zheng
,
Jianping Li
,
Hao Wang
,
Yuxun Li
,
Xiaoning Liu
, and
Rui Wang

Abstract

As the understanding of decadal variability in climate systems deepens, there is a growing interest in investigating the decadal variability of seasonal-mean or monthly mean variables. This study aims to understand the seasonality observed in the amplitude of decadal variability. To accomplish this, we analyze the decadal variability of the monthly mean North Atlantic Oscillation (NAO) index and North Pacific index (NPI) over the past decades using two different calculating processes: the full smoothing (F) process and the seasonal-specific (SS) process. Our findings suggest that the F process only captures the decadal variability of annual-mean variables, whereas the SS process is suited for capturing the seasonality of decadal variability. We find that the seasonality in decadal variability aligns with the seasonality in interannual variability. Additionally, we explore the seasonality in decadal variability in atmospheric and oceanic variables. The seasonality in oceanic decadal variability, including sea surface temperature and salinity, is found to be weak and small. The amplitude of decadal variability in the Pacific decadal oscillation (PDO) is similar across different months, indicating weak seasonality in the PDO. On the other hand, decadal variability of lower-tropospheric atmospheric circulation, including horizontal wind, geopotential height, and surface air temperature, exhibits significant seasonality in the extratropics, with the strongest decadal variability occurring in winter. Moreover, the significant seasonality in decadal variability of precipitation is observed in the tropics, with the strongest decadal variability occurring in summer. Our study provides insights into understanding the seasonality of decadal variability, which can aid in the improvement of decadal prediction of climate variability.

Significance Statement

The amplitude of decadal variability of seasonal-mean or monthly mean variables exhibits seasonality. Our results show that the seasonality in decadal variability is consistent with the seasonality in interannual variability. We also identified that the seasonality in oceanic decadal variability is weak and smaller than that in atmospheric decadal variability. Decadal variability of lower-tropospheric atmospheric circulation exhibits significant seasonality in the extratropics, with the strongest decadal variability occurring in winter. However, the significant seasonality in decadal variability of precipitation occurs in the tropics, with the strongest decadal variability occurring in summer. Our study provides insights into the seasonality of decadal variability in climate systems.

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Vibha Selvaratnam
,
David J. Thomson
, and
Helen N. Webster

Abstract

The Met Office’s atmospheric dispersion model Numerical Atmospheric-Dispersion Modeling Environment (NAME) is validated against controlled tracer release experiments, considering the impact of the driving meteorological data and choices in the parameterization of unresolved motions. The Cross-Appalachian Tracer Experiment (CAPTEX) and Across North America Tracer Experiment (ANATEX) were long-range dispersion experiments in which inert tracers were released and the air concentrations measured across North America in the 1980s. NAME simulations of the experiments have been driven by both reanalysis meteorological data from European Centre for Medium-Range Weather Forecasts (ECMWF) and data from the Advanced Research version of the Weather Research and Forecasting (WRF) Model. NAME predictions of air concentrations are assessed against the experimental measurements, using a ranking method composed of four statistical parameters. Differences in the performance of NAME according to this ranking method are compared when driven by different meteorological sources. The effect of changing parameter values in NAME for the unresolved mesoscale motions parameterization is also considered, in particular, whether the parameter values giving the best performance rank are consistent with values typically used. The performance ranks are compared with analyses in the literature for other particle dispersion models, namely, Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), Stochastic Time-Inverted Lagrangian Transport (STILT), and Flexible Particle (FLEXPART). It is found that NAME performance is comparable to the other dispersion models considered, with the different models responding similarly to differences in driving meteorological data.

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Free access
Veljko Petković
,
Paula J. Brown
,
Wesley Berg
,
David L. Randel
,
Spencer R. Jones
, and
Christian D. Kummerow

Abstract

Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%.

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