Monthly Temperature Extremes Research Articles

Regional temperature extremes and vulnerability under net zero CO2 emissions

Signatories to the Paris Agreement have pledged to keep global warming to well below 2 °C above pre-industrial levels and preferably below 1.5 °C above pre-industrial levels. Beyond over-shooting Paris Agreement warming levels followed by net negative emissions, achieving a state of net zero carbon dioxide emissions is required to satisfy Paris Agreement warming goals. Research on climate changes under net zero CO2 emissions is very limited to date with no comprehensive analysis of changes in extremes. In this study, we use results from Earth System Models in the zero emissions commitment model intercomparison project to understand regional mean-state climate change patterns during a 100 year period following carbon dioxide emissions cessation. We also perform an initial study of the evolution of hot and cold monthly temperature extremes after net zero CO2 emissions, including an assessment of how the change in frequency of temperature extremes affects areas of different levels of socioeconomic development based on regional Human Development Index (HDI). The results show that most land regions experience a fast and continuous cooling response following emissions cessation, with large areas of significant model agreement. In contrast, the Southern Ocean continues warming over the century after emissions cessation. The frequency of land-based local monthly high temperature extremes generally stays constant or decreases during the century after emissions cessation, however, decreases in heat extreme frequencies are generally less for locations with lower modern HDI than areas with higher HDI which suggests that inequality of climate change will remain an issue even after net zero CO2 emissions. There is an evident emergence of local monthly cold extremes following emissions cessation with most significant impact over high HDI mid- and high-latitude land regions.

New records of monthly temperature extremes as a signal of climate change in Italy

In a global warming regime, particular attention must be paid to possible changes in prolonged temperature extreme events on the regional scale, due to their strong impacts. But, while the increase in mean temperature is statistically significant and unequivocal, we do not know if the behaviour of extreme events is another significant signal of climate change or if it is still compatible with a “constant climate”, that is, with a stationary distribution of temperatures. Here, we propose a method to study the extremes through the analysis of the trend of the new historical records of mean monthly temperatures, and apply it to data about Italy, a country affected by a warming stronger than the global mean. A deep analysis of a 56‐year data set for 54 Italian stations and the use of Monte‐Carlo simulations permits us to achieve the evidence of a climate change also from the analysis of extremes. This analysis is performed at both the national level and for clusters of stations. The results show that the number of warm records increases more than what is expected by a constant climate “law,” in some months reaching or even overpassing the 95th percentile: May, June, July, and especially August appear as the most significant in this respect. Vice versa, the number of cold records lies often close to the fifth percentile or even under it; furthermore, in many months the cold records are very few after the middle of the 1990s. Some months can be, however, seen as “stationary islands.” For instance, the behaviour of the number of warm records in December and January follows closely the hypothesis of a constant climate, even if, in contrast, the cold records in January lie under the fifth percentile.

Temperature extremes and infant mortality in Bangladesh: Hotter months, lower mortality.

BackgroundOur study aims to obtain estimates of the size effects of temperature extremes on infant mortality in Bangladesh using monthly time series data.MethodsData on temperature, child and infant mortality were obtained for Matlab district of rural Bangladesh for January 1982 to December 2008 encompassing 49,426 infant deaths. To investigate the relationship between mortality and temperature, we adopted a regression with Autoregressive Integrated Moving Average (ARIMA) errors model of seasonally adjusted temperature and mortality data. The relationship between monthly mean and maximum temperature on infant mortality was tested at 0 and 1 month lags respectively. Furthermore, our analysis was stratified to determine if the results differed by gender (boys versus girls) and by age (neonates (≤ 30 days) versus post neonates (>30days and <153days)). Dickey Fuller tests were performed to test for stationarity, and since the time series were non-stationary, we conducted the regression analysis based on the first differences of mortality and temperature.ResultsHotter months were associated with lower infant mortality in Bangladesh. Each degree Celsius increase in mean monthly temperature reduced monthly mortality by 3.672 (SE 1.544, p<0.05) points. A one degree increase in mean monthly temperature one month prior reduced mortality by 0.767 (SE 0.439, p<0.1) for boys and by -0.0764 (SE 0.366, NS) for girls. Beneficial effects of maximum monthly temperature were on the order of 0.623 to -0.712 and statistically significant for girls and boys respectively. Effect sizes of mean monthly temperature were larger for neonates at 1.126 (SE 0.499, p<0.05) than for post-neonates at 0.880 (SE 0.310, p<0.05) reductions in mortality per degree.ConclusionThere is no evidence that infant survival is adversely affected by monthly temperature extremes in Bangladesh. This may reflect a more heightened sensitivity of infants to hypothermia than hyperthermia in this environment.

The Influence of Recurrent Modes of Climate Variability on the Occurrence of Monthly Temperature Extremes Over South America

AbstractThe associations between extreme temperature months and four prominent modes of recurrent climate variability are examined over South America. Associations are computed as the percent of extreme temperature months concurrent with the upper and lower quartiles of the El Niño–Southern Oscillation (ENSO), the Atlantic Niño, the Pacific Decadal Oscillation (PDO), and the Southern Annular Mode (SAM) index distributions, stratified by season. The relationship is strongest for ENSO, with nearly every extreme temperature month concurrent with the upper or lower quartiles of its distribution in portions of northwestern South America during some seasons. The likelihood of extreme warm temperatures is enhanced over parts of northern South America when the Atlantic Niño index is in the upper quartile, while cold extremes are often association with the lowest quartile. Concurrent precipitation anomalies may contribute to these relations. The PDO shows weak associations during December, January, and February, while in June, July, and August its relationship with extreme warm temperatures closely matches that of ENSO. This may be due to the positive relationship between the PDO and ENSO, rather than the PDO acting as an independent physical mechanism. Over Patagonia, the SAM is highly influential during spring and fall, with warm and cold extremes being associated with positive and negative phases of the SAM, respectively. Composites of sea level pressure anomalies for extreme temperature months over Patagonia suggest an important role of local synoptic scale weather variability in addition to a favorable SAM for the occurrence of these extremes.

Atmospheric circulation patterns associated with monthly and daily temperature and precipitation extremes in Alaska

ABSTRACTThis study presents (1) the temporal variations and trends of monthly and daily temperature and precipitation extremes in Alaska, and (2) the synoptic‐scale circulation patterns associated with these extremes. The analysis is based on daily station observations of maximum temperature, minimum temperature and precipitation from 1948–2012 as well as monthly average temperatures and precipitation from 1920–2012 from 13 Alaska climate divisions. The frequencies of daily and monthly temperature extremes show variability and trends over the past 50–90 years consistent with a warming climate in Alaska. This warming is superimposed on low‐frequency variability attributed to the Pacific Decadal Oscillation. Unlike the temperature extremes, occurrences of heavy‐precipitation extremes do not display a coherent signal through time. The changes do not appear to be linked with an overall increase in the variance of temperature or precipitation. Composite analysis of sea level pressure (SLP) anomalies associated with a representative sample of different daily and monthly temperature extremes across Alaska shows that temperature and moisture advection, subject to topographic and coastal influences, is a key driver of these events. Composite SLP fields for daily and monthly extremes are similar, especially in winter, indicating that monthly extremes represent the recurrence of daily extreme patterns during those extreme months. The SLP anomalies were also often reduced in magnitude in the summer events compared with winter, consistent with the lower variance of temperature and SLP during summer.

Modeling the Thermal Zones and Biodiversity on the High Mountains of Meganesia: The Importance of Local Differences

Alpine areas in Meganesia occur on maritime and continental mountains, and range from tropical to warm and cool temperate. At their current treelines, we measured soil temperatures, which were found to be within the bounds for treeline temperatures worldwide. We modeled areas above these alpine treelines using (1) a growing season mean synoptic temperature of 6.4 °C at treeline and (2) monthly temperature extremes. It was possible to adjust the threshold temperatures of the growing season model until predicted areas approximated observed alpine areas for New Guinea, the Australian mainland Snowy Mountains, and Tasmania, but meaningful predictions could not be made for the unknown alpine area of the Victorian Alps. The temperature extreme model was unsuitable for New Guinea and Tasmania but gave better predictive results for the Victorian Alps. We correctly predicted a strong relationship between alpine area and number of alpine vascular plant species and between regional area and number of terrestrial vertebrate species. However, there was no clear relationship between alpine area and alpine terrestrial vertebrate richness. Differences among the mountains were better explained by climatic extremes and insolation. If local explanations are required to model alpine vertebrate fauna, it is difficult to build robust global models and consequently make general predictions of climate change impacts.

Non-stationary return levels of CMIP5 multi-model temperature extremes

The objective of this study is to evaluate to what extent the CMIP5 climate model simulations of the climate of the twentieth century can represent observed warm monthly temperature extremes under a changing environment. The biases and spatial patterns of 2-, 10-, 25-, 50- and 100-year return levels of the annual maxima of monthly mean temperature (hereafter, annual temperature maxima) from CMIP5 simulations are compared with those of Climatic Research Unit (CRU) observational data considered under a non-stationary assumption. The results show that CMIP5 climate models collectively underestimate the mean annual maxima over arid and semi-arid regions that are most subject to severe heat waves and droughts. Furthermore, the results indicate that most climate models tend to underestimate the historical annual temperature maxima over the United States and Greenland, while generally disagreeing in their simulations over cold regions. Return level analysis shows that with respect to the spatial patterns of the annual temperature maxima, there are good agreements between the CRU observations and most CMIP5 simulations. However, the magnitudes of the simulated annual temperature maxima differ substantially across individual models. Discrepancies are generally larger over higher latitudes and cold regions.

An Archimedean Copula Family with Hyperbolic Cotangent Generator

Hyperbolic cotangent function is proposed as a generator of new Archimedean copula family and several properties are revealed. To show performance in real data analysis, application to modeling dependence between monthly temperature extremes as well as between flood peak and volume is given.

Comparison of Monthly Temperature Extremes Simulated by CMIP3 and CMIP5 Models

AbstractThe extreme monthly-mean temperatures simulated by 28 models in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated and compared with those from 24 models in the third phase of the Coupled Model Intercomparison Project (CMIP3). Comparisons with observations and reanalyses indicate that the models from both CMIP3 and CMIP5 perform well in simulating temperature extremes, which are expressed as 20-yr return values. When the climatological annual cycle is removed, the ensemble spread in CMIP5 is smaller than that in CMIP3. Benefitting from a higher resolution, the CMIP5 models perform better at simulating extreme temperatures on the local gridcell scale. The CMIP5 representative concentration pathway (RCP4.5) and CMIP3 B1 experiments project a similar change pattern in the near future for both warm and cold extremes, and the pattern is in agreement with that of the seasonal extremes. By the late twenty-first century, the changes in monthly temperature extremes projected under the three CMIP3 (B1, A1B, and A2) and two CMIP5 (RCP4.5 and RCP8.5) scenarios generally follow the changes in climatological annual cycles, which is consistent with previous studies on daily extremes. Compared with the CMIP3 ensemble, the CMIP5 ensemble shows a larger intermodel uncertainty with regard to the change in cold extremes in snow-covered regions. Enhanced changes in extreme temperatures that exceed the global mean warming are found in regions where the retreat of snow (or the soil moisture feedback effect) plays an important role, confirming the findings for daily temperature extremes.

Global increase in record-breaking monthly-mean temperatures

The last decade has produced record-breaking heat waves in many parts of the world. At the same time, it was globally the warmest since sufficient measurements started in the 19th century. Here we show that, worldwide, the number of local record-breaking monthly temperature extremes is now on average five times larger than expected in a climate with no long-term warming. This implies that on average there is an 80 % chance that a new monthly heat record is due to climatic change. Large regional differences exist in the number of observed records. Summertime records, which are associated with prolonged heat waves, increased by more than a factor of ten in some continental regions including parts of Europe, Africa, southern Asia and Amazonia. Overall, these high record numbers are quantitatively consistent with those expected for the observed climatic warming trend with added stationary white noise. In addition, we find that the observed records cluster both in space and in time. Strong El Nino years see additional records superimposed on the expected long-term rise. Under a medium global warming scenario, by the 2040s we predict the number of monthly heat records globally to be more than 12 times as high as in a climate with no long-term warming.

About the observed and future changes in temperature extremes over India

An attempt is made in the present study to analyse observed and model simulated temperature extremes over Indian region. Daily maximum and minimum temperature data at 121 well-distributed stations for the period 1970–2003 have been used to study the observed changes in objectively defined values of temperature extremes. In addition, an assessment of future scenarios of temperature extremes associated with increase in the concentration of atmospheric greenhouse gases is done using simulations of a state-of-the-art regional climate modelling system known as PRECIS (Providing Regional Climate for Impact Studies) performed to generate the climate for the present (1961–1990) and future projections for the period 2071–2100. Observational analysis done with 121 stations suggests the widespread warming through increase in intensity and frequency of hot events and also with decrease in frequency of cold events. More than 75% stations show decreasing trend in number of cold events and about 70% stations show increasing trend in hot events. Percentage of stations towards the warming through intensity indices of highest maximum temperature, lowest minimum temperature is 78 and 71% stations, respectively. Remaining stations show opposite trends, however, most of them are statistically insignificant. Observational analysis for India as a whole also shows similar results. Composite anomalies for monthly temperature extremes over two equal parts of the data period show increase (decrease) in the frequency of hot (cold) events for all months. In general, PRECIS simulations under both A2 and B2 scenarios indicate increase (decrease) in hot (cold) extremes towards the end of twenty-first century. Both show similar patterns, but the B2 scenario shows slightly lower magnitudes of the projected changes. Temperatures are likely to increase in entire calendar year, but the changes in winter season are expected to be prominent. Diurnal temperature range is expected to decrease in winter (JF) and pre-monsoon (MAM) months.

Why is the productivity of Douglas-fir higher in New Zealand than in its native range in the Pacific Northwest, USA?

Douglas-fir ( Pseudotusga menziesii (Mirb.) Franco), a native to the Pacific Coast Range in North America, is recognized as a tree that is long-lived and can grow rapidly to standing volumes that approach the highest recorded for temperate conifers. Managed plantations in western Oregon register maximum periodic annual increments (PAI) at ages between 20 and 40 years of ∼30 m 3 ha −1 year −1. The same seed source, when planted in New Zealand and elsewhere in the Southern Hemisphere, may attain a PAI of ∼50 m 3 ha −1 year −1. Is this higher productivity mainly related to climate or to isolation from native pests? To evaluate the role of climate, we obtained meteorological data from plantation sites in New Zealand and Oregon, from which we established relationships between mean monthly temperature extremes and solar irradiance, air humidity deficits, and frost frequency. Using these empirical relationships, long-term weather records were converted to the meteorological variables required to drive a process-based forest growth model, 3-PG, for sites approaching the most productive in New Zealand and in Oregon. Annual precipitation is similar in both areas, but sites in Oregon receive only 10% during the growing season, resulting in humidity deficits 30% larger than those recorded in New Zealand. According to sensitivity analyses and direct measurements, the more productive Douglas-fir forests in Oregon avoid the limiting effects of summer drought by obtaining water from the subsoil and fractured bedrock. If such forests were under a reduced evaporative demand similar to that in New Zealand, we show that they would exhibit comparable productivity.

A Multivariate Chain Model for Simulating Climatic Parameters from Daily Data

A method is described for simultaneously simulating maximum and minimum temperatures and daily precipitation amounts in a physically consistent manner. The method “chains” actual days from a historical dataset by defining a “discriminant space” using multiple discriminant analysis. A set of analogous days is selected from discriminant space using a nearest-neighbor search. The next day in the chain is the day subsequent to a randomly selected day from the set of analogous days. The method was tested on data for Tucson and Safford, Arizona. A high degree of similarity between the simulated and observed data was found. A slight tendency to underestimate the variance of monthly average temperatures was noted. The distribution of monthly temperature extremes was quite well reproduced with the exception of a tendency to be conservative in predicting the warmest minimum temperatures and the coolest maximum temperatures. Very little difference between the simulated and observed distributions of diurnal temperature range was found. The median and 90th percentile of monthly precipitation totals were well reproduced. A tendency to underestimate the frequency of dry months was noted. The frequency of runs of wet and dry days of different lengths was found to be not significantly different for the simulated and observed data. Reproduction of wet-day run frequency for the first-order multivariate chain model was comparable to that using a two-state, first-order Markov chain.