scholarly journals Two-Meter Temperature and Precipitation from Atmospheric Reanalysis Evaluated for Alaska

2016 ◽  
Vol 55 (4) ◽  
pp. 901-922 ◽  
Author(s):  
Rick Lader ◽  
Uma S. Bhatt ◽  
John E. Walsh ◽  
T. Scott Rupp ◽  
Peter A. Bieniek
Keyword(s):  
Global Climate ◽  
Weather Forecast ◽  
Heavy Precipitation ◽  
Reanalysis Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Monthly Data ◽  
Temperature And Precipitation ◽  
Grid Points

AbstractAlaska is experiencing effects of global climate change that are due, in large part, to the positive feedback mechanisms associated with polar amplification. The major risk factors include loss of sea ice and glaciers, thawing permafrost, increased wildfires, and ocean acidification. Reanalyses, integral to understanding mechanisms of Alaska’s past climate and to helping to calibrate modeling efforts, are based on the output of weather forecast models that assimilate observations. This study evaluates temperature and precipitation from five reanalyses at monthly and daily time scales for the period 1979–2009. Monthly data are evaluated spatially at grid points and for six climate zones in Alaska. In addition, daily maximum temperature, minimum temperature, and precipitation from reanalyses are compared with meteorological-station data at six locations. The reanalyses evaluated in this study include the NCEP–NCAR reanalysis (R1), North American Regional Reanalysis (NARR), Climate Forecast System Reanalysis (CFSR), ERA-Interim, and the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Maps of seasonal bias and standard deviation, constructed from monthly data, show how the reanalyses agree with observations spatially. Cross correlations between the monthly gridded and daily station time series are computed to provide a measure of confidence that data users can assume when selecting reanalysis data in a region without many surface observations. A review of natural hazards in Alaska indicates that MERRA is the top reanalysis for wildfire and interior-flooding applications. CFSR is the recommended reanalysis for North Slope coastal erosion issues and, along with ERA-Interim, for heavy precipitation in southeastern Alaska.

scholarly journals Joint Occurrence of Daily Temperature and Precipitation Extreme Events over Canada

2014 ◽  
Vol 53 (9) ◽  
pp. 2148-2162 ◽  
Author(s):  
Bárbara Tencer ◽  
Andrew Weaver ◽  
Francis Zwiers
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Negative Consequences ◽  
Temperature And Precipitation ◽  
Joint Occurrence

AbstractThe occurrence of individual extremes such as temperature and precipitation extremes can have a great impact on the environment. Agriculture, energy demands, and human health, among other activities, can be affected by extremely high or low temperatures and by extremely dry or wet conditions. The simultaneous or proximate occurrence of both types of extremes could lead to even more profound consequences, however. For example, a dry period can have more negative consequences on agriculture if it is concomitant with or followed by a period of extremely high temperatures. This study analyzes the joint occurrence of very wet conditions and high/low temperature events at stations in Canada. More than one-half of the stations showed a significant positive relationship at the daily time scale between warm nights (daily minimum temperature greater than the 90th percentile) or warm days (daily maximum temperature above the 90th percentile) and heavy-precipitation events (daily precipitation exceeding the 75th percentile), with the greater frequencies found for the east and southwest coasts during autumn and winter. Cold days (daily maximum temperature below the 10th percentile) occur together with intense precipitation more frequently during spring and summer. Simulations by regional climate models show good agreement with observations in the seasonal and spatial variability of the joint distribution, especially when an ensemble of simulations was used.

scholarly journals Population Exposure to Compound Droughts and Heatwaves in the Observations and ERA5 Reanalysis Data in the Gan River Basin, China

Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1021
Author(s):  
Yuqing Zhang ◽  
Guangxiong Mao ◽  
Changchun Chen ◽  
Liucheng Shen ◽  
Binyu Xiao
Keyword(s):  
River Basin ◽  
Population Change ◽  
Reanalysis Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Circulation System ◽  
Population Exposure ◽  
Daily Maximum ◽  
The One ◽  
Background Fields

The frequency, duration, and magnitude of heatwaves and droughts are expected to increase in a warming climate, which can have profound impacts on the environment, society, and public health, and these may be severely affected specifically by compound droughts and heatwaves (CDHWs). On the basis of daily maximum temperature data and the one-month standardized precipitation evapotranspiration index (SPEI) from 1961 to 2018, the Gan River Basin (GRB) was taken as a case here to construct CDHW identification indicators and quantify the population exposure to CDHWs. We found that ERA5 reanalysis data performed well in overall simulating temperature, precipitation, one-month SPEI, heatwaves, and CDHWs in the GRB from 1961 to 2018. CDHWs during the period from 1997 to 2018 were slightly higher than that in 1961–1997. CDHWs were more likely to occur in the southern parts of the basin due to the relatively high values of drought–heatwave dependence indices. Atmospheric circulation analysis of the 2003 CDHW in the GRB showed a relatively long-lasting anomalous high pressure and anticyclonic circulation system, accompanied by the positive convective inhibition and surface net solar radiation anomalies. These circulating background fields eventually led to the exceptional 2003 CDHW occurrence in the GRB. The population exposure to CDHWs basically increased, especially for the moderate CDHWs in ERA5. The change in total exposure was mainly due to climate change. Compared with the period from 1989 to 1998, the contributions of the population change effect in 2009–2018 gradually increased with the increase in the CDHW magnitude both in the observations and ERA5 reanalysis data.

scholarly journals Response of shortwave cloud radiative effect to greenhouse gases and aerosols and its impact on daily maximum temperature

2020 ◽  
Author(s):  
Tao Tang ◽  
Drew Shindell ◽  
Yuqiang Zhang ◽  
Apostolos Voulgarakis ◽  
Jean-Francois Lamarque ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Boreal Summer ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Sulfate Aerosols ◽  
Multilinear Regression

Abstract. Shortwave cloud radiative effects (SWCRE), defined as the difference of shortwave radiative flux between all-sky and clear-sky conditions, have been reported to play an important role in influencing the Earth’s energy budget and temperature extremes. In this study, we employed a set of global climate models to examine the SWCRE responses to CO2, black carbon (BC) aerosols and sulfate aerosols in boreal summer over the Northern Hemisphere. We found that CO2 causes positive SWCRE changes over most of the NH, and BC causes similar positive responses over North America, Europe and East China but negative SWCRE over India and tropical Africa. When normalized by effective radiative forcing, the SWCRE from BC is roughly 3–5 times larger than that from CO2. SWCRE change is mainly due to cloud cover changes resulting from the changes in relative humidity (RH) and, to a lesser extent, changes in circulation and stability. The SWCRE response to sulfate aerosols, however, is negligible compared to that for CO2 and BC. Using a multilinear regression model, it is found that mean daily maximum temperature (Tmax) increases by 0.15 K and 0.13 K per W m−2 increase in local SWCRE under the CO2 and BC experiment, respectively. When domain-averaged, the SWCRE change contribution to summer mean Tmax changes was 10–30 % under CO2 forcing and 30–50 % under BC forcing, varying by region, which can have important implications for extreme climatic events and socio-economic activities.

scholarly journals Improved Bias Correction Techniques for Hydrological Simulations of Climate Change*

2015 ◽  
Vol 16 (6) ◽  
pp. 2421-2442 ◽  
Author(s):  
David W. Pierce ◽  
Daniel R. Cayan ◽  
Edwin P. Maurer ◽  
John T. Abatzoglou ◽  
Katherine C. Hegewisch
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Model ◽  
Global Climate Model ◽  
Cumulative Distribution ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Climate Change Signal ◽  
Daily Maximum ◽  
Temperature And Precipitation

Abstract Global climate model (GCM) output typically needs to be bias corrected before it can be used for climate change impact studies. Three existing bias correction methods, and a new one developed here, are applied to daily maximum temperature and precipitation from 21 GCMs to investigate how different methods alter the climate change signal of the GCM. The quantile mapping (QM) and cumulative distribution function transform (CDF-t) bias correction methods can significantly alter the GCM’s mean climate change signal, with differences of up to 2°C and 30% points for monthly mean temperature and precipitation, respectively. Equidistant quantile matching (EDCDFm) bias correction preserves GCM changes in mean daily maximum temperature but not precipitation. An extension to EDCDFm termed PresRat is introduced, which generally preserves the GCM changes in mean precipitation. Another problem is that GCMs can have difficulty simulating variance as a function of frequency. To address this, a frequency-dependent bias correction method is introduced that is twice as effective as standard bias correction in reducing errors in the models’ simulation of variance as a function of frequency, and it does so without making any locations worse, unlike standard bias correction. Last, a preconditioning technique is introduced that improves the simulation of the annual cycle while still allowing the bias correction to take account of an entire season’s values at once.

scholarly journals Spatiotemporal Changes in Temperature and Precipitation in West Africa. Part I: Analysis with the CMIP6 Historical Dataset

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3506
Author(s):  
Gandomè Mayeul Leger Davy Quenum ◽  
Francis Nkrumah ◽  
Nana Ama Browne Klutse ◽  
Mouhamadou Bamba Sylla
Keyword(s):  
West Africa ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Upward Trend ◽  
Extreme Climate ◽  
Temperature And Precipitation ◽  
Extreme Indices

Climate variability and change constitute major challenges for Africa, especially West Africa (WA), where an important increase in extreme climate events has been noticed. Therefore, it appears essential to analyze characteristics and trends of some key climatological parameters. Thus, this study addressed spatiotemporal variabilities and trends in regard to temperature and precipitation extremes by using 21 models of the Coupled Model Intercomparison Project version 6 (CMIP6) and 24 extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). First, the CMIP6 variables were evaluated with observations (CHIRPS, CHIRTS, and CRU) of the period 1983–2014; then, the extreme indices from 1950 to 2014 were computed. The innovative trend analysis (ITA), Sen’s slope, and Mann–Kendall tests were utilized to track down trends in the computed extreme climate indices. Increasing trends were observed for the maxima of daily maximum temperature (TXX) and daily minimum temperature (TXN) as well as the maximum and minimum of the minimum temperature (TNX and TNN). This upward trend of daily maximum temperature (Tmax) and daily minimum temperature (Tmin) was enhanced with a significant increase in warm days/nights (TX90p/TN90p) and a significantly decreasing trend in cool days/nights (TX10p/TN10p). The precipitation was widely variable over WA, with more than 85% of the total annual water in the study domain collected during the monsoon period. An upward trend in consecutive dry days (CDD) and a downward trend in consecutive wet days (CWD) influenced the annual total precipitation on wet days (PRCPTOT). The results also depicted an upward trend in SDII and R30mm, which, additionally to the trends of CDD and CWD, could be responsible for localized flood-like situations along the coastal areas. The study identified the 1970s dryness as well as the slight recovery of the 1990s, which it indicated occurred in 1992 over West Africa.

scholarly journals Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures

2018 ◽  
Vol 4 (1/2) ◽  
pp. 37-52
Author(s):  
Rasmus E. Benestad ◽  
Bob van Oort ◽  
Flavio Justino ◽  
Frode Stordal ◽  
Kajsa M. Parding ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Principal Component ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Climate Conditions ◽  
Probability Of Occurrence

Abstract. A methodology for estimating and downscaling the probability associated with the duration of heatwaves is presented and applied as a case study for Indian wheat crops. These probability estimates make use of empirical-statistical downscaling and statistical modelling of probability of occurrence and streak length statistics, and we present projections based on large multi-model ensembles of global climate models from the Coupled Model Intercomparison Project Phase 5 and three different emissions scenarios: Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5. Our objective was to estimate the probabilities for heatwaves with more than 5 consecutive days with daily maximum temperature above 35 ∘C, which represent a condition that limits wheat yields. Such heatwaves are already quite frequent under current climate conditions, and downscaled estimates of the probability of occurrence in 2010 is in the range of 20 %–84 % depending on the location. For the year 2100, the high-emission scenario RCP8.5 suggests more frequent occurrences, with a probability in the range of 36 %–88 %. Our results also point to increased probabilities for a hot day to turn into a heatwave lasting more than 5 days, from roughly 8 %–20 % at present to 9 %–23 % in 2100 assuming future emissions according to the RCP8.5 scenario; however, these estimates were to a greater extent subject to systematic biases. We also demonstrate a downscaling methodology based on principal component analysis that can produce reasonable results even when the data are sparse with variable quality.

scholarly journals Increase of Extreme Drought over Ethiopia under Climate Warming

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Asaminew Teshome ◽  
Jie Zhang
Keyword(s):  
Heavy Precipitation ◽  
Extreme Drought ◽  
Precipitation Extremes ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Temperature And Precipitation ◽  
Increasing Trend ◽  
Annual Total

Recurrent extreme drought and flood in Ethiopia lead to more economic loss. This study examines change and trends of 21 climate extremes of temperature and precipitation over Ethiopia by using indices from the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). The analysis was based on the records of observed meteorological data and the future projected from the CMIP5 model under RCP 4.5 and RCP 8.5 scenarios. The results of the seasonal standardized rainfall anomaly and EOF analysis show a decreasing rainfall in JJAS season and significant variability in the FMAM season. The first mode of EOF in FMAM shows that 49.6% was mostly negative with a high amount of variability. The observed precipitation extreme of annual total precipitation (PRCPTOT), consecutive wet days (CWD), and the number of heavy precipitation days (R10) show a decreasing trend, and consecutive dry days (CDD) shows an increasing trend. Additionally, temperature extremes like tropical nights (TR20) and daily maximum and minimum temperatures show a significantly increasing trend. The projected precipitation extremes of CWD, PRCPTOT, very wet day annual total (R95p), and the number of heavy precipitation days (R10) show a decreasing trend. CDD shows longer periods of dryness and a substantial increase which is conducive to the increase of drought. The projected temperature extremes of the warm spell duration indicator (WSDI), daily maximum temperature (TXx) and daily minimum temperature (TNx), summer days (SU25), and tropical nights (TR20) show an increasing trend, while the diurnal temperature range shows a decreasing trend. The projected changes in temperature and precipitation extremes are likely to have significant negative impacts on various socioeconomic activities over Ethiopia. These results highlight the need for planning and developing effective adaptation strategies for disaster prevention.

scholarly journals Impact of Aligning Climatological Day on Gridding Daily Maximum–Minimum Temperature and Precipitation over Canada

2011 ◽  
Vol 50 (8) ◽  
pp. 1654-1665 ◽  
Author(s):  
Ron F. Hopkinson ◽  
Daniel W. McKenney ◽  
Ewa J. Milewska ◽  
Michael F. Hutchinson ◽  
Pia Papadopol ◽  
...  
Keyword(s):  
Minimum Temperature ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Daily Minimum ◽  
Temperature And Precipitation ◽  
Daily Data ◽  
National University ◽  
Maximum Temperatures

AbstractOn 1 July 1961, the climatological day was redefined to end at 0600 UTC at all principal climate stations in Canada. Prior to that, the climatological day at principal stations ended at 1200 UTC for maximum temperature and precipitation and 0000 UTC for minimum temperature and was similar to the climatological day at ordinary stations. Hutchinson et al. reported occasional larger-than-expected residuals at 50 withheld stations when the Australian National University Spline (ANUSPLIN) interpolation scheme was applied to daily data for 1961–2003, and it was suggested that these larger residuals were in part due to the existence of different climatological days. In this study, daily minimum and maximum temperatures at principal stations were estimated using hourly temperatures for the same climatological day as local ordinary climate stations for the period 1953–2007. Daily precipitation was estimated at principal stations using synoptic precipitation data for the climatological day ending at 1200 UTC, which, for much of the country, was close to the time of the morning observation at ordinary climate stations. At withheld principal stations, the climatological-day adjustments led to the virtual elimination of large residuals in maximum and minimum temperature and a marked reduction in precipitation residuals. Across all 50 withheld stations the climatological day adjustments led to significant reductions, by around 12% for daily maximum temperature, 15% for daily minimum temperature, and 22% for precipitation, in the residuals reported by Hutchinson et al.

scholarly journals Return levels of temperature extremes in southern Pakistan

2017 ◽  
Vol 8 (4) ◽  
pp. 1263-1278 ◽  
Author(s):  
Maida Zahid ◽  
Richard Blender ◽  
Valerio Lucarini ◽  
Maria Caterina Bramati
Keyword(s):  
Reanalysis Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Extreme Temperatures ◽  
Peaks Over Threshold ◽  
Return Levels ◽  
Rural And Urban ◽  
The World

Abstract. Southern Pakistan (Sindh) is one of the hottest regions in the world and is highly vulnerable to temperature extremes. In order to improve rural and urban planning, it is useful to gather information about the recurrence of temperature extremes. In this work, return levels of the daily maximum temperature Tmax are estimated, as well as the daily maximum wet-bulb temperature TWmax extremes. We adopt the peaks over threshold (POT) method, which has not yet been used for similar studies in this region. Two main datasets are analyzed: temperatures observed at nine meteorological stations in southern Pakistan from 1980 to 2013, and the ERA-Interim (ECMWF reanalysis) data for the nearest corresponding locations. The analysis provides the 2-, 5-, 10-, 25-, 50-, and 100-year return levels (RLs) of temperature extremes. The 90 % quantile is found to be a suitable threshold for all stations. We find that the RLs of the observed Tmax are above 50 °C at northern stations and above 45 °C at the southern stations. The RLs of the observed TWmax exceed 35 °C in the region, which is considered as a limit of survivability. The RLs estimated from the ERA-Interim data are lower by 3 to 5 °C than the RLs assessed for the nine meteorological stations. A simple bias correction applied to ERA-Interim data improves the RLs remarkably, yet discrepancies are still present. The results have potential implications for the risk assessment of extreme temperatures in Sindh.

A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain

2010 ◽  
Vol 19 (3) ◽  
pp. 325 ◽  
Author(s):  
Lara Vilar ◽  
Douglas. G. Woolford ◽  
David L. Martell ◽  
M. Pilar Martín
Keyword(s):  
Regional Scale ◽  
Additive Model ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Generalised Additive Model ◽  
Wildfire Occurrence ◽  
Temperature And Precipitation ◽  
Spatio Temporal ◽  
Development And Validation

This paper describes the development and validation of a spatio-temporal model for human-caused wildfire occurrence prediction at a regional scale. The study area is the 8028-km2 region of Madrid, located in central Spain, where more than 90% of wildfires are caused by humans. We construct a logistic generalised additive model to estimate daily fire ignition risk at a 1-km2 grid spatial resolution. Spatially referenced socioeconomic and weather variables appear as covariates in the model. Spatial and temporal effects are also included. The variables in the model were selected using an iterative approach, which we describe. We use the model to predict the expected number of fires in our study area during the 2002–05 period, by aggregating the estimated probabilities over space–time scales of interest. The estimated partial effects of the presence of railways, roads, and wildland–urban interface in forest areas were highly significant, as were the observed daily maximum temperature and precipitation.

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