Trend analysis of tropospheric specific humidity over Iran during 1979–2016

Darand, Mohammad; Farshad, Pazhooh; Saligheh, Mohammad

doi:10.1002/joc.6059

Cited by 15 publications

(3 citation statements)

References 32 publications

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“…By the end of the 21st Century, the area-averaged TN90p and TX90p are projected to increase 79% and 74%, respectively. This may be due to an increase of air moisture in the atmosphere in this region by an increase in air temperature, as illustrated by Darand et al (2019). The projected changes in extreme temperature indices over Iran will be helpful for policymakers due to the vast implications for planning and appropriate strategies to adapt to the projected changes.…”

Section: Discussionmentioning

confidence: 99%

Future changes in temperature extremes in climate variability over Iran

Darand

2020

Meteorological Applications

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The objective of the current study was to project changes in extreme temperature indices over Iran by the end of the 21st Century based on the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations. We used six extreme temperature indices recommended and defined by the Expert Team on Climate Change Detection and Indices from a set of 18 CMIP5 model simulations from different modelling centres. The extreme temperature indices simulation results of the models were compared with the gridded extreme temperature indices observations of the Asfazari National Database with a spatial resolution of 15 × 15 km during the period 1962–2005. For comparison purposes, the CMIP5 model data and the Asfazari data were regridded to a uniform horizontal resolution 0.5° × 0.5° grid using a bilinear interpolation scheme before further processing. After first validating the model performance by computing bias, future changes in the period 2006–2100 were projected. The modified Mann Kendall trend test and the Sen slope estimator test were used for evaluating the trend of indices and estimating the change rate, respectively, at a 95% confidence level. The results show that the minimum of the daily minimum temperature (TNn) and the minimum of the daily maximum temperature (TXn) will increase in the future. Warming of night time temperature indices (TNn and TN90p) are projected to be stronger than those of daytime temperature indices (TXn and TX90p). Warming is not uniform across the country, with considerably more warming in the western and southern parts. In terms of the frequency of frost days, a decrease is generally projected in the 21st Century. The largest decrease is projected for the Zagros mountain ranges in the west and the mountainous region in the northeastern part of the country with a magnitude of around 8–10 days⋅decade−1. The number of heat wave days is expected to increase sharply from about 13 days in the reference period (1963–2005) to 100 days by mid‐century to more than 235 days by the end of the century under Representative Concentration Pathway 8.5.

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Section: Discussionmentioning

confidence: 99%

Future changes in temperature extremes in climate variability over Iran

Darand

2020

Meteorological Applications

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“…However, in order to eliminate the influence of significant autocorrelation in the series of data, the researchers employed the modified MK test, in which the series were prewhitened (Jahani et al, 2018). The magnitude of trends were estimated by using a nonparametric Theil-Sen's slope procedure (Sen, 1968;Theil, 1992;Jahani et al, 2018), which is a well-known test (Darand et al, 2019;Delvaux et al, 2019;Yang et al, 2019).…”

Section: Statistical Analysesmentioning

confidence: 99%

Trend analysis of extreme precipitation events across Iran using percentile indices

Mahbod

Rafiee

2020

Intl Journal of Climatology

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Although a number of studies have been conducted on extreme precipitation trends in different parts of the world including Iran, a great number of such studies have reported only the total amount of daily precipitation greater than a certain percentage (e.g., 95%) of the long term data (R95p), ignoring other useful indices. To address this research gap, we used other modified indices, namely R95tot (fractional contribution of very wet days to annual total amounts), R95tt (fractional contribution of very wet days to the total annual obtained from fitted gamma probability distribution), and RS95 (same as R95tt except that it uses Weibull distribution and very wet days defined by 95 percentage of an individual year) by which the spatial and temporal changes of very wet days across Iran was assessed, 1985–2013. In addition, to evaluate the effect of the selected distribution on the results, a new index‐(RS95gm)—was introduced and reported. This index is similar to RS95, except that it uses gamma distribution instead of Weibull. According to trend analysis of R95p, R95tot, and R95tt, reduced frequency of extreme precipitation events was detected in some northwest, west and northeast parts of Iran. On the contrary, RS95 (RS95gm) results showed a higher frequency of extreme events across Iran. It was also demonstrated that while R95p, R95tot, and R95tt were unequivocally affected by changes in the mean wet‐day/ annual total precipitation, RS95 (RS95gm) was more influenced by changes in the distributional shape, showing more stable trends. Although RS95 and RS95gm were highly correlated with only 19% difference on average, their trend analysis results were not completely consistent (70% agreement). Thus, it may be concluded that any changes in statistical distribution in the calculation of the RS95 would have a considerable effect on whether the obtained trend is significant or not.

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“…By contrast, there was an increase, over both land and ocean, in specific humidity and precipitable water in response to the increased temperatures both at the surface and in the troposphere (Gaffen et al 1991;Trenberth et al 2005;Masson-Delmotte et al 2021), with the temperature-moisture relationship being modified by changes in dynamical processes, for example poleward expansion of the Hadley cell (Nguyen et al 2015;Tabari 2020). On the regional scale, Gaffen et al (1991) and Elliott (1996, 2001) reported an increase in specific humidity over most of North America and a small decrease over Europe; Darand et al (2019) found a decrease in specific humidity over most of Iran apart from coastal areas; Hense et al (1988) reported an upward trend of 700-500-hPa precipitable water and RH over the western Pacific; McCarthy et al (2009) found that homogenised radiosonde data show an increase in specific humidity over northern hemisphere extra-tropics; whereas Xie et al (2011) and Zhao et al (2012) found an increase in precipitable water associated with the slight warming in the lower to mid-troposphere over China.…”

Section: Introductionmentioning

confidence: 99%

A historical monthly upper-air humidity dataset for Australia

Jovanovic

Smalley

Siems

2023

J. South. Hemisph. Earth Syst. Sci.

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Monthly humidity (represented as dew point temperature, DWPT) data from 22 land and 5 island Australian upper-air sites were analysed, with trends estimated over the 1965–2017 period at four pressure levels. Humidity data were selected to ensure that data collected under consistent sampling conditions were used (‘modified data’). The quality control process involved examining station metadata and applying an objective statistical test that detected discontinuities in the data series. At each station and pressure level, modified data series were adjusted (homogenised) on a monthly timescale when discontinuities were identified. Analysis of the homogenised (adjusted) modified DWPT data indicates that, over the 1965–2017 period, linear trends are mostly positive and smaller compared to unadjusted modified data. The all-Australian time-series show positive trends at the 850–400-hPa levels. The total increases in DWPT since 1965 at 850-, 700-, 500- and 400-hPa levels are ~0.5, ~1.2, ~1.3 and ~0.8°C respectively. The increase in humidity in the lower and middle troposphere is in accordance with the expectation that, as the troposphere warms, the amount of moisture in it should increase (at a differential rate of ~7% °C–1 at low altitudes globally, following Clausius–Clapeyron scaling) due to increasing surface evaporation and moisture-holding capacity of the air. However, changes in atmospheric dynamics also influence the magnitude and distribution of humidity trends. The homogenised modified Australian radiosonde data for the 850-hPa level show that the amount of moisture at this level increased ~8.8% °C–1 during 1965–2015

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Trend analysis of tropospheric specific humidity over Iran during 1979–2016

Cited by 15 publications

References 32 publications

Future changes in temperature extremes in climate variability over Iran

Future changes in temperature extremes in climate variability over Iran

Trend analysis of extreme precipitation events across Iran using percentile indices

A historical monthly upper-air humidity dataset for Australia

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