Surface relative humidity (RH) is a key element for weather and climate monitoring and research. However, RH is not as commonly applied in studying climate change, partly because the observation ...series of RH are prone to inhomogeneous biases due to non-climate changes in the observation system. A homogenized dataset of daily RH series from 746 stations in Chinese mainland for the period 1960–2017, ChinaRHv1.0, has been developed. Most (685 or 91.82% of the total) station time series were inhomogeneous with one or more break points. The major breakpoints occurred in the early 2000s for many stations, especially in the humid and semi-humid zones, due to the implementation of automated observation across the country. The inhomogeneous biases in the early manual records before this change are positive relative to the recent automatic records, for most of the biased station series. There are more break points detected by using the MASH (Multiple Analysis of Series for Homogenization) method, with biases mainly around −0.5% and 0.5%. These inhomogeneous biases are adjusted with reference to the most recent observations for each station. Based on the adjusted observations, the regional mean RH series of China shows little long-term trend during 1960–2017 0.006% (10 yr)
−1
, contrasting with a false decreasing trend −0.414% (10 yr)
−1
in the raw data. It is notable that ERA5 reanalysis data match closely with the interannual variations of the raw RH series in China, including the jump in the early 2000s, raising a caveat for its application in studying climate change in the region.
Recent trends in summer heat waves (HW) over Central-Eastern China and their atmospheric drivers are investigated using the ERA Interim re-analysis. A composite analysis shows that these events are ...preceded by an increase in 500 hPa geopotential height. Consequently, a subsidence anomaly develops over the region and surface solar radiation increases. An increase in the northward moisture transport from the tropical region is also found to increase specific humidity, leading to warmer night-time temperatures. Feedback effects are also important: decrease of precipitation and enhanced evaporation also increases the specific humidity and North-Westerlies due to the low pressure lead to more heat convergence. HW occurrence increases, especially during the last decade, and is largely due to an increase in the mean temperature rather than to a change in dynamics, suggesting a human influence.
Abstract
Wet heatwaves can have more impact on human health than hot dry heatwaves. However, changes in these have received little scientific attention. Using the ECMWF Reanalysis v5 reanalysis ...dataset, wet-bulb temperatures (
T
w
) were used to investigate the spatial-temporal variation of wet heatwaves in Eurasia for 1979–2017. Wet heatwaves were defined as three day or longer periods when
T
w
was above the 90th percentile of the summer distribution and characterized by amplitude, duration and frequency. Maximum values of amplitude, close to 31 °C, occur in the Indus–Ganges plain, the lower Yangtze valley, and the coasts of the Persian Gulf and Red Sea. Significant positive trends in the frequency and amplitude of wet heatwaves have occurred over most of Eurasia though with regional variations. Changes in heatwave amplitude (HWA) are largely driven by changes in summer mean
T
w
. For Eurasia as a whole, increases in temperature contribute more than six times the impact of changes in relative humidity (RH) to changes in
T
w
HWA. Changes in
T
w
have a strong dependence on climatological RH with an increase in RH of 1% causing a
T
w
increase of 0.2 °C in arid regions, and only increasing
T
w
by 0.1 °C in humid regions. During
T
w
heatwaves in Europe, parts of Tibet, India, East Asia and parts of the Arabian Peninsula both temperature and humidity contribute to the increase in
T
w
, with temperature the dominant driver. During wet heatwaves in part of Russia, changes in humidity are weak and the increase in
T
w
is mainly caused by an increase in temperature. In the Mediterranean and Central Asia, RH has fallen reducing the increase in
T
w
from general warming.
Extreme temperature events causing significant environmental and humanitarian impacts are expected to increase in frequency and magnitude due to global warming. The latest generation of climate model ...projections, Coupled Model Intercomparison Project Phase Six (CMIP6), provides a new and improved database to investigate change in future daily scale extreme temperature events. This study examines the changes in 1, 3, and 5 day averaged annual maximum temperature in four large CMIP6 ensembles. It analyses, using a generalized extreme value (GEV) method, the change in extreme daily mean temperatures at 1.5 and 2°C of global warming, levels highlighted by the 2016 Paris Agreement, and additionally at 3°C. Extremely hot events are characterized using the annual maxima of daily near surface air temperature in the SSP370 scenario. Global changes in the mode of the distributions (location parameter) follow long‐term summer warming and show very similar spatial patterns. Changes in variability (scale parameter) show a clear trend of increases over the tropics and decreases over higher latitudes, while changes to the tails of distributions (shape parameter) show less globally consistent trends but clear signals over the Arctic sea ice, behaviour also seen in variability. Risk ratios (RRs) indicating the change in probability of hot daily extremes that currently have a 10 year return period increase globally with mean temperature change, with greater increases over the tropics. Globally averaged changes in RR over land range from 3.1–3.6 to 7.9–8.3 for 1.5 and 3°C of warming, respectively. For the latter case, this indicates previously rare, once‐in‐a‐decade summer extremes will occur almost annually in the future under high warming.
Future extreme heat events are expected to increase significantly with global warming, causing far‐reaching humanitarian, environmental and economic damage. We examine the characteristics of future daily scale annual maxima by applying the generalized extreme value distribution to data sets obtained from CMIP6 daily model output at 1.5, 2, and 3°C of warming. Our study shows significant increases in the probabilities of current events globally, with substantial regions seeing current 1‐in‐10 year events annually at higher levels of warming.
Change in extreme events in climate projections is a major concern. If the frequency of dry events is expected to increase in a warmer climate (thus, the overall number of wet days will decrease), ...heavy and extreme precipitation are also expected to increase because of a shift of the precipitation spectrum. However, the forecasts exhibit numerous uncertainties.
This study focuses on the Asian region, separated into the following three subregions: the East Asian region, the Indian region, and western North Pacific region, where the summer monsoon can bring heavy rainfall. Particularly emphasized herein is the reliability of the projection, using data from a large ensemble of 30 models from phase 5 of the Coupled Model Intercomparison Project. The scattering of the ensemble enables obtaining an optimal estimate of the uncertainties, and it is used to compute the correlation between projected changes of extreme events and circulation changes.
The results show clear spatial and temporal variations in the confidence of changes, with results being more reliable during the wet season (i.e., the summer monsoon). The ensemble predicts changes in atmospheric circulation with favorable confidence, especially in the low-level moisture flux convergence (MFC). However, the correlation between this mean change and the modification of extreme events is nonsignificant. Also analyzed herein are the correlation and change of MFC exclusively during these events. The horizontal MFC exerts a nonnegligible influence on the change in the intensity of extremes. However, it is mostly the change in vertical circulation and moisture advection that is correlated with the change in frequency and intensity of extreme events.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Extreme heat, particularly if combined with humidity, poses a severe risk to human health. To estimate future global risk of extreme heat with humidity on health, we calculate indicators of ...heat stress that have been commonly used: the Heat Index, the Wet-Bulb Globe Temperature and the Wet-Bulb Temperature, from the latest Climate Model Intercomparison Project (CMIP6) projections. We analyse how and where different levels of heat stress hazards will change, from severe to deadly, and how results are sensitive to the choice of the index used. We evaluate this risk at country-level and use population and GDP
|
PPP growth scenario to estimate the vulnerability of each nation. Consistent with previous studies, we find that South and East Asia, and the Middle-East, are highly exposed to heat stress hazards, and that this exposure increases by 20%–60% with global mean temperature change from 1.5 to 3
∘
C. However, we also find substantial increases in heat health risk for some vulnerable countries with less adaptive capacity, such as West Africa, and Central and South America. For these regions, about 20 to more than 50% of the population could be exposed to severe heat stress each year on average, independent of the index used. For global warming of 3
∘
, European countries and the USA will also be exposed several times per year to conditions with daily mean heat stress level equal to the maximum heat stress of the 2003 heat wave.
The seasonal evolution of the upper tropospheric South Asian high follows and influences underlying summer monsoon advancement. A strong connection between the South Asian high and westerly ...perturbation to the north suggests further planetary-scale dynamical control of the monsoon. In the mid-1990s, a clear location shift of the South Asian high in May–June was noted and was observed in fewer (more) frequencies of the high centers over the Indochina Peninsula (Iranian Plateau). Continental confinement of monsoonal circulation and precipitation was observed during 1995–2010, as opposed to larger-scale development in the Asia–Pacific region during 1979–1994. In view of early-summer monsoon evolution, a westward shifting and faster migration of the South Asian high may imply increased control of the midlatitude dynamics. By contrast, the convection over the tropical Western North Pacific (WNP) has an opposite and delayed contribution to monsoon advancement. After the mid-1990s than it had been previously, the midlatitude jet stream largely weakened over northern Africa and the East Asia–Pacific region, corresponding to an increase in the upper tropospheric geopotential heights north of the jet stream. Climate model experiments further reveal that the warming over Europe–Asia and temperature change in the North Atlantic can result in the change in midlatitude perturbations and the monsoon evolution in the mid-1990s, suggesting large-scale and dynamic impact on monsoon climatology.
May 2016 was the third wettest May on record since 1961 over central eastern China based on station observations, with total monthly rainfall 40% more than the climatological mean for 1961-2013. ...Accompanying disasters such as waterlogging, landslides and debris flow struck part of the lower reaches of the Yangtze River. Causal influence of anthropogenic forcings on this event is investigated using the newly updated Met Office Hadley Centre system for attribution of extreme weather and climate events. Results indicate that there is a significant increase in May 2016 rainfall in model simulations relative to the climatological period, but this increase is largely attributable to natural variability. El Niño years have been found to be correlated with extreme rainfall in the Yangtze River region in previous studies-the strong El Niño of 2015-2016 may account for the extreme precipitation event in 2016. However, on smaller spatial scales we find that anthropogenic forcing has likely played a role in increasing the risk of extreme rainfall to the north of the Yangtze and decreasing it to the south.
Abstract
Atmospheric blocking (‘blocking’) in the Northern Hemisphere (NH) is a crucial driver of extreme cold spells in winter. Here we investigate the anthropogenic influence on the NH blocking and ...its impact on surface air temperature (SAT) during the winter 1960/1961–2012/2013 using two HadGEM3-GA6-N216 simulations with 15 ensemble members: (a) with anthropogenic and natural forcing (All-hist) and (b) with natural forcing only (Nat-hist). Compared to the Nat-hist run, the blocking frequency in the All-hist run decreases in the Euro-Atlantic, the Urals and the western Pacific, whereas it increases in the eastern Pacific and Greenland. These responses can be explained by the response of planetary waves and storm tracks. On the other hand, the decrease in SAT downstream of the blocking regions in the All-hist run is more pronounced than the Nat-hist run, especially in Europe and the Urals. Correspondingly, the proportion of cold days during all blocking days in these sectors is higher in the All-hist run than the Nat-hist run. These responses can be explained by the wind response associated with blocking. Overall, the spatiotemporal characteristics of blocking is crucial for evaluating the impact of blocking on extreme weather, and their response to anthropogenic forcing should be investigated by more models.
On 21-25 July 2017 a record-breaking heatwave occurred in Central Eastern China, affecting nearly half of the national population and causing severe impacts on public health, agriculture and ...infrastructure. Here, we compare attribution results from two UK Met Office Hadley Centre models, HadGEM3-GA6 and weather@home (HadAM3P driving 50 km HadRM3P). Within HadGEM3-GA6 July 2017-like heatwaves were unequaled in the ensemble representing the world without human influences. Such heatwaves became approximately a 1 in 50 year event and increased by a factor of 4.8 (5%-95% range of 3.1 to 8.0) in weather@home as a result of human activity. Considering the risk ratio (RR) for the full range of return periods shows a discrepancy at all return times between the two model results. Within weather@home a range of different counterfactual sea surface temperature (SST) patterns were used, whereas HadGEM3-GA6 used a single estimate. The global mean difference in SST (between factual and counterfactual simulations) is shown to be related to the generalised extreme value (GEV) location parameter and consequently the RR, especially for return periods of less than 50 years. It is suggested that a suitable range of SST patterns are used for future attribution studies to ensure that this source of uncertainty is represented within the simulations and subsequent attribution results. It is shown that the risk change between factual and counterfactual simulations is not purely a simple shift in the distribution (i.e. change in GEV location parameter). For return periods greater than 50 years, the GEV shape parameter is found to strongly influence the RR determined with the GEV scale parameter affecting only the most severe events.
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